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Language Of Research

Learning about research is a lot like learning about anything else. To start, you need to learn the jargon people use, the big controversies they fight over, and the different factions that define the major players. We’ll start by considering five really big multi-syllable words that researchers sometimes use to describe what they do. We’ll only do a few for now, to give you an idea of just how esoteric the discussion can get (but not enough to cause you to give up in total despair). We can then take on some of the major issues in research like the types of questions we can ask in a project, the role of time in research , and the different types of relationships we can estimate. Then we have to consider defining some basic terms like variable , hypothesis , data , and unit of analysis . If you’re like me, you hate learning vocabulary, so we’ll quickly move along to consideration of two of the major fallacies of research, just to give you an idea of how wrong even researchers can be if they’re not careful (of course, there’s always a certainly probability that they’ll be wrong even if they’re extremely careful).

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The Language of Research: an Explanation and Examples

Learning the language of research can help you understand research  answers to important problems. It can also help you read academic texts (and tests) more easily.   

This page explains key words for understanding the process of research. It talks about what can go wrong, leading to false or misleading results. 

Then it links to several examples of research , so you can see how scientists use these words.

At the bottom is a link to a crossword puzzle . Use it to check your understanding of many important words used in research reports.

The Research Process

Research starts with a question or a problem. Researchers first find out what others have already learned about the subject.

If the question has not been fully answered, they figure out a way to get more information. They may do further observations or perform an experiment to test their idea.

Next they analyze the data (information) they have collected. Then they publish their procedures, data, and conclusions. This allows other scientists to repeat the experiments and double-check the conclusions.

The “gold standard” (best proof) of clinical research is a double-blind trial. That is an experiment with two (or more) groups of people in which only one group receives the drug or treatment being tested. The other group gets a placebo. 

(A placebo is a “sugar pill” or other treatment that looks and feels like the experimental treatment but has no active ingredients.  Any effects it has are psychological—because the participants expect it to work.)

A “double-blind” experiment gets its name because both the researchers and the participants are “blind” during the test. Nobody knows until the experiment has finished which group got the treatment and which group got an inactive placebo. That helps prevent people's expectations from distorting (twisting or changing) the results.

The treatment being tested should give significantly better results than the placebo. If not, any apparent difference it makes may be due to people’s hopes and expectations. So a double-blind trial is a way to check the effectiveness of a treatment.

The Language of Research: Describing Problems (Bias, Errors, and Distortion)

The language of research: describing errors.

There are several reasons research results can be misleading. There may be flaws in the research design. Researchers may make mistakes during the experiment or when analyzing the data. They may even be biased: wanting certain results so much that they influence the results.

Sometimes groups that might profit from the results pay for the research but onl report it if they get the results they want.

Here is  an article , and then  a TED talk , that explain these problems in more depth. (They aso show how to use the language for errors.) The article emphasizes the importance of having other researchers repeat important experiments. If other researchers get the same results, people can have more confidence in them. 

The TED talk gives some clues to recognize when some research results may be biased or missing. (The speaker presents some very useful information. However, he talks quickly and uses a lot of British slang. If you find listening difficult, try reading the written transcript of his talk.) 

Examples of the Language of Research  

Research Study 1: Drug Interactions

Doctors know a lot about the side effects of single medications. They have also studied some interactions between two drugs. But what can happen when a person takes more than two (as many patients with serious health problems do)?  This talk  discusses the need for more research on drug interactions, even when each is safe by itself.

Study 2: The Effects of Green Spaces on Health

This study finds major health benefits for city residents who live near parks.     

Study 3: Forest Re-growth Helps the Climate  

This article  discusses how the regeneration of tropical forests can reduce global warming. With more trees, more carbon dioxide is taken out of the air and stored in the trees.

Practice the Language of Research with a Crossword 

Right-click here for the crossword , and here for the answers .

You can practice more research vocabulary with Scientific Method Vocabulary , as well as Science Vocabulary , or  a gap-fill practice on climate change at  Conservation Terminology .

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Language and Scientific Research

• © 2021
• Wenceslao J. Gonzalez 0

Center for Research in Philosophy of Science and Technology, University of A Coruña, Ferrol, Spain

You can also search for this editor in PubMed   Google Scholar

Provides a comprehensive exploration of the semantics of science

Covers both basic scientific research and the development of applied science

Argues that language affects the structure and dynamics of science and is therefore not a mere expressive instrumental sign

1578 Accesses

3 Citations

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Table of contents (9 chapters)

Front matter, the relevance of language for scientific research.

Wenceslao J. Gonzalez

The Problem of Reference and Potentialities of the Language in Science

Semantics of science and theory of reference: an analysis of the role of language in basic science and applied science, on the role of language in scientific research: language as analytic, expressive, and explanatory tool.

• Ladislav Kvasz

Language and Change in Scientific Research: Evolution and Historicity

Scientific inquiry and the evolution of language.

• Jeffrey Barrett

Language, History and the Making of Accurate Observations

• Anastasios Brenner

Scientific Language in the Context of Truth and Fiction

The evolution of truth and belief, models, fictions and artifacts.

• Tarja Knuuttila

Language in Mathematics and in Empirical Sciences

On mathematical language: characteristics, semiosis and indispensability.

• Jesus Alcolea

Characterization of Scientific Prediction from Language: An Analysis of Nicholas Rescher’s Proposal

• Amanda Guillan

Back Matter

• Historicity
• Mathhematics

About this book

This book analyzes the role of language in scientific research and develops the semantics of science from different angles. The philosophical investigation of the volume is divided into four parts, which covers both basic science and applied science: I) The Problem of Reference and Potentialities of the Language in Science; II) Language and Change in Scientific Research: Evolution and Historicity; III) Scientific Language in the Context of Truth and Fiction; and IV) Language in Mathematics and in Empirical Sciences.

Editors and Affiliations

About the editor, bibliographic information.

Book Title : Language and Scientific Research

Editors : Wenceslao J. Gonzalez

DOI : https://doi.org/10.1007/978-3-030-60537-7

Publisher : Palgrave Macmillan Cham

eBook Packages : Religion and Philosophy , Philosophy and Religion (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Hardcover ISBN : 978-3-030-60536-0 Published: 28 April 2021

Softcover ISBN : 978-3-030-60539-1 Published: 28 April 2022

eBook ISBN : 978-3-030-60537-7 Published: 27 April 2021

Edition Number : 1

Number of Pages : XI, 285

Number of Illustrations : 15 b/w illustrations

Topics : Philosophy of Science , Philosophy of Language

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Article contents

Language and power.

• Sik Hung Ng Sik Hung Ng Department of Psychology, Renmin University of China
•  and  Fei Deng Fei Deng School of Foreign Studies, South China Agricultural University
• https://doi.org/10.1093/acrefore/9780190228613.013.436
• Published online: 22 August 2017

Five dynamic language–power relationships in communication have emerged from critical language studies, sociolinguistics, conversation analysis, and the social psychology of language and communication. Two of them stem from preexisting powers behind language that it reveals and reflects, thereby transferring the extralinguistic powers to the communication context. Such powers exist at both the micro and macro levels. At the micro level, the power behind language is a speaker’s possession of a weapon, money, high social status, or other attractive personal qualities—by revealing them in convincing language, the speaker influences the hearer. At the macro level, the power behind language is the collective power (ethnolinguistic vitality) of the communities that speak the language. The dominance of English as a global language and international lingua franca, for example, has less to do with its linguistic quality and more to do with the ethnolinguistic vitality of English-speakers worldwide that it reflects. The other three language–power relationships refer to the powers of language that are based on a language’s communicative versatility and its broad range of cognitive, communicative, social, and identity functions in meaning-making, social interaction, and language policies. Such language powers include, first, the power of language to maintain existing dominance in legal, sexist, racist, and ageist discourses that favor particular groups of language users over others. Another language power is its immense impact on national unity and discord. The third language power is its ability to create influence through single words (e.g., metaphors), oratories, conversations and narratives in political campaigns, emergence of leaders, terrorist narratives, and so forth.

• power behind language
• power of language
• intergroup communication
• World Englishes
• oratorical power
• conversational power
• leader emergence
• al-Qaeda narrative
• social identity approach

Introduction

Language is for communication and power.

Language is a natural human system of conventionalized symbols that have understood meanings. Through it humans express and communicate their private thoughts and feelings as well as enact various social functions. The social functions include co-constructing social reality between and among individuals, performing and coordinating social actions such as conversing, arguing, cheating, and telling people what they should or should not do. Language is also a public marker of ethnolinguistic, national, or religious identity, so strong that people are willing to go to war for its defense, just as they would defend other markers of social identity, such as their national flag. These cognitive, communicative, social, and identity functions make language a fundamental medium of human communication. Language is also a versatile communication medium, often and widely used in tandem with music, pictures, and actions to amplify its power. Silence, too, adds to the force of speech when it is used strategically to speak louder than words. The wide range of language functions and its versatility combine to make language powerful. Even so, this is only one part of what is in fact a dynamic relationship between language and power. The other part is that there is preexisting power behind language which it reveals and reflects, thereby transferring extralinguistic power to the communication context. It is thus important to delineate the language–power relationships and their implications for human communication.

This chapter provides a systematic account of the dynamic interrelationships between language and power, not comprehensively for lack of space, but sufficiently focused so as to align with the intergroup communication theme of the present volume. The term “intergroup communication” will be used herein to refer to an intergroup perspective on communication, which stresses intergroup processes underlying communication and is not restricted to any particular form of intergroup communication such as interethnic or intergender communication, important though they are. It echoes the pioneering attempts to develop an intergroup perspective on the social psychology of language and communication behavior made by pioneers drawn from communication, social psychology, and cognate fields (see Harwood et al., 2005 ). This intergroup perspective has fostered the development of intergroup communication as a discipline distinct from and complementing the discipline of interpersonal communication. One of its insights is that apparently interpersonal communication is in fact dynamically intergroup (Dragojevic & Giles, 2014 ). For this and other reasons, an intergroup perspective on language and communication behavior has proved surprisingly useful in revealing intergroup processes in health communication (Jones & Watson, 2012 ), media communication (Harwood & Roy, 2005 ), and communication in a variety of organizational contexts (Giles, 2012 ).

The major theoretical foundation that has underpinned the intergroup perspective is social identity theory (Tajfel, 1982 ), which continues to service the field as a metatheory (Abrams & Hogg, 2004 ) alongside relatively more specialized theories such as ethnolinguistic identity theory (Harwood et al., 1994 ), communication accommodation theory (Palomares et al., 2016 ), and self-categorization theory applied to intergroup communication (Reid et al., 2005 ). Against this backdrop, this chapter will be less concerned with any particular social category of intergroup communication or variant of social identity theory, and more with developing a conceptual framework of looking at the language–power relationships and their implications for understanding intergroup communication. Readers interested in an intra- or interpersonal perspective may refer to the volume edited by Holtgraves ( 2014a ).

Conceptual Approaches to Power

Bertrand Russell, logician cum philosopher and social activist, published a relatively little-known book on power when World War II was looming large in Europe (Russell, 2004 ). In it he asserted the fundamental importance of the concept of power in the social sciences and likened its importance to the concept of energy in the physical sciences. But unlike physical energy, which can be defined in a formula (e.g., E=MC 2 ), social power has defied any such definition. This state of affairs is not unexpected because the very nature of (social) power is elusive. Foucault ( 1979 , p. 92) has put it this way: “Power is everywhere, not because it embraces everything, but because it comes from everywhere.” This view is not beyond criticism but it does highlight the elusiveness of power. Power is also a value-laden concept meaning different things to different people. To functional theorists and power-wielders, power is “power to,” a responsibility to unite people and do good for all. To conflict theorists and those who are dominated, power is “power over,” which corrupts and is a source of social conflict rather than integration (Lenski, 1966 ; Sassenberg et al., 2014 ). These entrenched views surface in management–labor negotiations and political debates between government and opposition. Management and government would try to frame the negotiation in terms of “power to,” whereas labor and opposition would try to frame the same in “power over” in a clash of power discourses. The two discourses also interchange when the same speakers reverse their power relations: While in opposition, politicians adhere to “power over” rhetorics, once in government, they talk “power to.” And vice versa.

The elusive and value-laden nature of power has led to a plurality of theoretical and conceptual approaches. Five approaches that are particularly pertinent to the language–power relationships will be discussed, and briefly so because of space limitation. One approach views power in terms of structural dominance in society by groups who own and/or control the economy, the government, and other social institutions. Another approach views power as the production of intended effects by overcoming resistance that arises from objective conflict of interests or from psychological reactance to being coerced, manipulated, or unfairly treated. A complementary approach, represented by Kurt Lewin’s field theory, takes the view that power is not the actual production of effects but the potential for doing this. It looks behind power to find out the sources or bases of this potential, which may stem from the power-wielders’ access to the means of punishment, reward, and information, as well as from their perceived expertise and legitimacy (Raven, 2008 ). A fourth approach views power in terms of the balance of control/dependence in the ongoing social exchange between two actors that takes place either in the absence or presence of third parties. It provides a structural account of power-balancing mechanisms in social networking (Emerson, 1962 ), and forms the basis for combining with symbolic interaction theory, which brings in subjective factors such as shared social cognition and affects for the analysis of power in interpersonal and intergroup negotiation (Stolte, 1987 ). The fifth, social identity approach digs behind the social exchange account, which has started from control/dependence as a given but has left it unexplained, to propose a three-process model of power emergence (Turner, 2005 ). According to this model, it is psychological group formation and associated group-based social identity that produce influence; influence then cumulates to form the basis of power, which in turn leads to the control of resources.

Common to the five approaches above is the recognition that power is dynamic in its usage and can transform from one form of power to another. Lukes ( 2005 ) has attempted to articulate three different forms or faces of power called “dimensions.” The first, behavioral dimension of power refers to decision-making power that is manifest in the open contest for dominance in situations of objective conflict of interests. Non-decision-making power, the second dimension, is power behind the scene. It involves the mobilization of organizational bias (e.g., agenda fixing) to keep conflict of interests from surfacing to become public issues and to deprive oppositions of a communication platform to raise their voices, thereby limiting the scope of decision-making to only “safe” issues that would not challenge the interests of the power-wielder. The third dimension is ideological and works by socializing people’s needs and values so that they want the wants and do the things wanted by the power-wielders, willingly as their own. Conflict of interests, opposition, and resistance would be absent from this form of power, not because they have been maneuvered out of the contest as in the case of non-decision-making power, but because the people who are subject to power are no longer aware of any conflict of interest in the power relationship, which may otherwise ferment opposition and resistance. Power in this form can be exercised without the application of coercion or reward, and without arousing perceived manipulation or conflict of interests.

Language–Power Relationships

As indicated in the chapter title, discussion will focus on the language–power relationships, and not on language alone or power alone, in intergroup communication. It draws from all the five approaches to power and can be grouped for discussion under the power behind language and the power of language. In the former, language is viewed as having no power of its own and yet can produce influence and control by revealing the power behind the speaker. Language also reflects the collective/historical power of the language community that uses it. In the case of modern English, its preeminent status as a global language and international lingua franca has shaped the communication between native and nonnative English speakers because of the power of the English-speaking world that it reflects, rather than because of its linguistic superiority. In both cases, language provides a widely used conventional means to transfer extralinguistic power to the communication context. Research on the power of language takes the view that language has power of its own. This power allows a language to maintain the power behind it, unite or divide a nation, and create influence.

In Figure 1 we have grouped the five language–power relationships into five boxes. Note that the boundary between any two boxes is not meant to be rigid but permeable. For example, by revealing the power behind a message (box 1), a message can create influence (box 5). As another example, language does not passively reflect the power of the language community that uses it (box 2), but also, through its spread to other language communities, generates power to maintain its preeminence among languages (box 3). This expansive process of language power can be seen in the rise of English to global language status. A similar expansive process also applies to a particular language style that first reflects the power of the language subcommunity who uses the style, and then, through its common acceptance and usage by other subcommunities in the country, maintains the power of the subcommunity concerned. A prime example of this type of expansive process is linguistic sexism, which reflects preexisting male dominance in society and then, through its common usage by both sexes, contributes to the maintenance of male dominance. Other examples are linguistic racism and the language style of the legal profession, each of which, like linguistic sexism and the preeminence of the English language worldwide, has considerable impact on individuals and society at large.

Space precludes a full discussion of all five language–power relationships. Instead, some of them will warrant only a brief mention, whereas others will be presented in greater detail. The complexity of the language–power relations and their cross-disciplinary ramifications will be evident in the multiple sets of interrelated literatures that we cite from. These include the social psychology of language and communication, critical language studies (Fairclough, 1989 ), sociolinguistics (Kachru, 1992 ), and conversation analysis (Sacks et al., 1974 ).

Figure 1. Power behind language and power of language.

Power Behind Language

Language reveals power.

When negotiating with police, a gang may issue the threatening message, “Meet our demands, or we will shoot the hostages!” The threatening message may succeed in coercing the police to submit; its power, however, is more apparent than real because it is based on the guns gangsters posses. The message merely reveals the power of a weapon in their possession. Apart from revealing power, the gangsters may also cheat. As long as the message comes across as credible and convincing enough to arouse overwhelming fear, it would allow them to get away with their demands without actually possessing any weapon. In this case, language is used to produce an intended effect despite resistance by deceptively revealing a nonexisting power base and planting it in the mind of the message recipient. The literature on linguistic deception illustrates the widespread deceptive use of language-reveals-power to produce intended effects despite resistance (Robinson, 1996 ).

Language Reflects Power

Ethnolinguistic vitality.

The language that a person uses reflects the language community’s power. A useful way to think about a language community’s linguistic power is through the ethnolinguistic vitality model (Bourhis et al., 1981 ; Harwood et al., 1994 ). Language communities in a country vary in absolute size overall and, just as important, a relative numeric concentration in particular regions. Francophone Canadians, though fewer than Anglophone Canadians overall, are concentrated in Quebec to give them the power of numbers there. Similarly, ethnic minorities in mainland China have considerable power of numbers in those autonomous regions where they are concentrated, such as Inner Mongolia, Tibet, and Xinjiang. Collectively, these factors form the demographic base of the language community’s ethnolinguistic vitality, an index of the community’s relative linguistic dominance. Another base of ethnolinguistic vitality is institutional representations of the language community in government, legislatures, education, religion, the media, and so forth, which afford its members institutional leadership, influence, and control. Such institutional representation is often reinforced by a language policy that installs the language as the nation’s sole official language. The third base of ethnolinguistic vitality comprises sociohistorical and cultural status of the language community inside the nation and internationally. In short, the dominant language of a nation is one that comes from and reflects the high ethnolinguistic vitality of its language community.

An important finding of ethnolinguistic vitality research is that it is perceived vitality, and not so much its objective demographic-institutional-cultural strengths, that influences language behavior in interpersonal and intergroup contexts. Interestingly, the visibility and salience of languages shown on public and commercial signs, referred to as the “linguistic landscape,” serve important informational and symbolic functions as a marker of their relative vitality, which in turn affects the use of in-group language in institutional settings (Cenoz & Gorter, 2006 ; Landry & Bourhis, 1997 ).

World Englishes and Lingua Franca English

Another field of research on the power behind and reflected in language is “World Englishes.” At the height of the British Empire English spread on the back of the Industrial Revolution and through large-scale migrations of Britons to the “New World,” which has since become the core of an “inner circle” of traditional native English-speaking nations now led by the United States (Kachru, 1992 ). The emergent wealth and power of these nations has maintained English despite the decline of the British Empire after World War II. In the post-War era, English has become internationalized with the support of an “outer circle” nations and, later, through its spread to “expanding circle” nations. Outer circle nations are made up mostly of former British colonies such as India, Pakistan, and Nigeria. In compliance with colonial language policies that institutionalized English as the new colonial national language, a sizeable proportion of the colonial populations has learned and continued using English over generations, thereby vastly increasing the number of English speakers over and above those in the inner circle nations. The expanding circle encompasses nations where English has played no historical government roles, but which are keen to appropriate English as the preeminent foreign language for local purposes such as national development, internationalization of higher education, and participation in globalization (e.g., China, Indonesia, South Korea, Japan, Egypt, Israel, and continental Europe).

English is becoming a global language with official or special status in at least 75 countries (British Council, n.d. ). It is also the language choice in international organizations and companies, as well as academia, and is commonly used in trade, international mass media, and entertainment, and over the Internet as the main source of information. English native speakers can now follow the worldwide English language track to find jobs overseas without having to learn the local language and may instead enjoy a competitive language advantage where the job requires English proficiency. This situation is a far cry from the colonial era when similar advantages had to come under political patronage. Alongside English native speakers who work overseas benefitting from the preeminence of English over other languages, a new phenomenon of outsourcing international call centers away from the United Kingdom and the United States has emerged (Friginal, 2007 ). Callers can find the information or help they need from people stationed in remote places such as India or the Philippines where English has penetrated.

As English spreads worldwide, it has also become the major international lingua franca, serving some 800 million multilinguals in Asia alone, and numerous others elsewhere (Bolton, 2008 ). The practical importance of this phenomenon and its impact on English vocabulary, grammar, and accent have led to the emergence of a new field of research called “English as a lingua franca” (Brosch, 2015 ). The twin developments of World Englishes and lingua franca English raise interesting and important research questions. A vast area of research lies in waiting.

Several lines of research suggest themselves from an intergroup communication perspective. How communicatively effective are English native speakers who are international civil servants in organizations such as the UN and WTO, where they habitually speak as if they were addressing their fellow natives without accommodating to the international audience? Another line of research is lingua franca English communication between two English nonnative speakers. Their common use of English signals a joint willingness of linguistic accommodation, motivated more by communication efficiency of getting messages across and less by concerns of their respective ethnolinguistic identities. An intergroup communication perspective, however, would sensitize researchers to social identity processes and nonaccommodation behaviors underneath lingua franca communication. For example, two nationals from two different countries, X and Y, communicating with each other in English are accommodating on the language level; at the same time they may, according to communication accommodation theory, use their respective X English and Y English for asserting their ethnolinguistic distinctiveness whilst maintaining a surface appearance of accommodation. There are other possibilities. According to a survey of attitudes toward English accents, attachment to “standard” native speaker models remains strong among nonnative English speakers in many countries (Jenkins, 2009 ). This suggests that our hypothetical X and Y may, in addition to asserting their respective Englishes, try to outperform one another in speaking with overcorrect standard English accents, not so much because they want to assert their respective ethnolinguistic identities, but because they want to project a common in-group identity for positive social comparison—“We are all English-speakers but I am a better one than you!”

Many countries in the expanding circle nations are keen to appropriate English for local purposes, encouraging their students and especially their educational elites to learn English as a foreign language. A prime example is the Learn-English Movement in China. It has affected generations of students and teachers over the past 30 years and consumed a vast amount of resources. The results are mixed. Even more disturbing, discontents and backlashes have emerged from anti-English Chinese motivated to protect the vitality and cultural values of the Chinese language (Sun et al., 2016 ). The power behind and reflected in modern English has widespread and far-reaching consequences in need of more systematic research.

Power of Language

Language maintains existing dominance.

Language maintains and reproduces existing dominance in three different ways represented respectively by the ascent of English, linguistic sexism, and legal language style. For reasons already noted, English has become a global language, an international lingua franca, and an indispensable medium for nonnative English speaking countries to participate in the globalized world. Phillipson ( 2009 ) referred to this phenomenon as “linguistic imperialism.” It is ironic that as the spread of English has increased the extent of multilingualism of non-English-speaking nations, English native speakers in the inner circle of nations have largely remained English-only. This puts pressure on the rest of the world to accommodate them in English, the widespread use of which maintains its preeminence among languages.

A language evolves and changes to adapt to socially accepted word meanings, grammatical rules, accents, and other manners of speaking. What is acceptable or unacceptable reflects common usage and hence the numerical influence of users, but also the elites’ particular language preferences and communication styles. Research on linguistic sexism has shown, for example, a man-made language such as English (there are many others) is imbued with sexist words and grammatical rules that reflect historical male dominance in society. Its uncritical usage routinely by both sexes in daily life has in turn naturalized male dominance and associated sexist inequalities (Spender, 1998 ). Similar other examples are racist (Reisigl & Wodak, 2005 ) and ageist (Ryan et al., 1995 ) language styles.

Professional languages are made by and for particular professions such as the legal profession (Danet, 1980 ; Mertz et al., 2016 ; O’Barr, 1982 ). The legal language is used not only among members of the profession, but also with the general public, who may know each and every word in a legal document but are still unable to decipher its meaning. Through its language, the legal profession maintains its professional dominance with the complicity of the general public, who submits to the use of the language and accedes to the profession’s authority in interpreting its meanings in matters relating to their legal rights and obligations. Communication between lawyers and their “clients” is not only problematic, but the public’s continual dependence on the legal language contributes to the maintenance of the dominance of the profession.

Language Unites and Divides a Nation

A nation of many peoples who, despite their diverse cultural and ethnic background, all speak in the same tongue and write in the same script would reap the benefit of the unifying power of a common language. The power of the language to unite peoples would be stronger if it has become part of their common national identity and contributed to its vitality and psychological distinctiveness. Such power has often been seized upon by national leaders and intellectuals to unify their countries and serve other nationalistic purposes (Patten, 2006 ). In China, for example, Emperor Qin Shi Huang standardized the Chinese script ( hanzi ) as an important part of the reforms to unify the country after he had defeated the other states and brought the Warring States Period ( 475–221 bc ) to an end. A similar reform of language standardization was set in motion soon after the overthrow of the Qing Dynasty ( ad 1644–1911 ), by simplifying some of the hanzi and promoting Putonghua as the national standard oral language. In the postcolonial part of the world, language is often used to service nationalism by restoring the official status of their indigenous language as the national language whilst retaining the colonial language or, in more radical cases of decolonization, relegating the latter to nonofficial status. Yet language is a two-edged sword: It can also divide a nation. The tension can be seen in competing claims to official-language status made by minority language communities, protest over maintenance of minority languages, language rights at schools and in courts of law, bilingual education, and outright language wars (Calvet, 1998 ; DeVotta, 2004 ).

Language Creates Influence

In this section we discuss the power of language to create influence through single words and more complex linguistic structures ranging from oratories and conversations to narratives/stories.

Power of Single Words

Learning a language empowers humans to master an elaborate system of conventions and the associations between words and their sounds on the one hand, and on the other hand, categories of objects and relations to which they refer. After mastering the referential meanings of words, a person can mentally access the objects and relations simply by hearing or reading the words. Apart from their referential meanings, words also have connotative meanings with their own social-cognitive consequences. Together, these social-cognitive functions underpin the power of single words that has been extensively studied in metaphors, which is a huge research area that crosses disciplinary boundaries and probes into the inner workings of the brain (Benedek et al., 2014 ; Landau et al., 2014 ; Marshal et al., 2007 ). The power of single words extends beyond metaphors. It can be seen in misleading words in leading questions (Loftus, 1975 ), concessive connectives that reverse expectations from real-world knowledge (Xiang & Kuperberg, 2014 ), verbs that attribute implicit causality to either verb subject or object (Hartshorne & Snedeker, 2013 ), “uncertainty terms” that hedge potentially face-threatening messages (Holtgraves, 2014b ), and abstract words that signal power (Wakslak et al., 2014 ).

The literature on the power of single words has rarely been applied to intergroup communication, with the exception of research arising from the linguistic category model (e.g., Semin & Fiedler, 1991 ). The model distinguishes among descriptive action verbs (e.g., “hits”), interpretative action verbs (e.g., “hurts”) and state verbs (e.g., “hates”), which increase in abstraction in that order. Sentences made up of abstract verbs convey more information about the protagonist, imply greater temporal and cross-situational stability, and are more difficult to disconfirm. The use of abstract language to represent a particular behavior will attribute the behavior to the protagonist rather than the situation and the resulting image of the protagonist will persist despite disconfirming information, whereas the use of concrete language will attribute the same behavior more to the situation and the resulting image of the protagonist will be easier to change. According to the linguistic intergroup bias model (Maass, 1999 ), abstract language will be used to represent positive in-group and negative out-group behaviors, whereas concrete language will be used to represent negative in-group and positive out-group behaviors. The combined effects of the differential use of abstract and concrete language would, first, lead to biased attribution (explanation) of behavior privileging the in-group over the out-group, and second, perpetuate the prejudiced intergroup stereotypes. More recent research has shown that linguistic intergroup bias varies with the power differential between groups—it is stronger in high and low power groups than in equal power groups (Rubini et al., 2007 ).

Oratorical Power

A charismatic speaker may, by the sheer force of oratory, buoy up people’s hopes, convert their hearts from hatred to forgiveness, or embolden them to take up arms for a cause. One may recall moving speeches (in English) such as Susan B. Anthony’s “On Women’s Right to Vote,” Winston Churchill’s “We Shall Fight on the Beaches,” Mahatma Gandhi’s “Quit India,” or Martin Luther King, Jr.’s “I Have a Dream.” The speech may be delivered face-to-face to an audience, or broadcast over the media. The discussion below focuses on face-to-face oratories in political meetings.

Oratorical power may be measured in terms of money donated or pledged to the speaker’s cause, or, in a religious sermon, the number of converts made. Not much research has been reported on these topics. Another measurement approach is to count the frequency of online audience responses that a speech has generated, usually but not exclusively in the form of applause. Audience applause can be measured fairly objectively in terms of frequency, length, or loudness, and collected nonobtrusively from a public recording of the meeting. Audience applause affords researchers the opportunity to explore communicative and social psychological processes that underpin some aspects of the power of rhetorical formats. Note, however, that not all incidences of audience applause are valid measures of the power of rhetoric. A valid incidence should be one that is invited by the speaker and synchronized with the flow of the speech, occurring at the appropriate time and place as indicated by the rhetorical format. Thus, an uninvited incidence of applause would not count, nor is one that is invited but has occurred “out of place” (too soon or too late). Furthermore, not all valid incidences are theoretically informative to the same degree. An isolated applause from just a handful of the audience, though valid and in the right place, has relatively little theoretical import for understanding the power of rhetoric compared to one that is made by many acting in unison as a group. When the latter occurs, it would be a clear indication of the power of rhetorically formulated speech. Such positive audience response constitutes the most direct and immediate means by which an audience can display its collective support for the speaker, something which they would not otherwise show to a speech of less power. To influence and orchestrate hundreds and thousands of people in the audience to precisely coordinate their response to applaud (and cheer) together as a group at the right time and place is no mean feat. Such a feat also influences the wider society through broadcast on television and other news and social media. The combined effect could be enormous there and then, and its downstream influence far-reaching, crossing country boarders and inspiring generations to come.

To accomplish the feat, an orator has to excite the audience to applaud, build up the excitement to a crescendo, and simultaneously cue the audience to synchronize their outburst of stored-up applause with the ongoing speech. Rhetorical formats that aid the orator to accomplish the dual functions include contrast, list, puzzle solution, headline-punchline, position-taking, and pursuit (Heritage & Greatbatch, 1986 ). To illustrate, we cite the contrast and list formats.

A contrast, or antithesis, is made up of binary schemata such as “too much” and “too little.” Heritage and Greatbatch ( 1986 , p. 123) reported the following example:

Governments will argue that resources are not available to help disabled people. The fact is that too much is spent on the munitions of war, and too little is spent on the munitions of peace [italics added]. As the audience is familiar with the binary schema of “too much” and “too little” they can habitually match the second half of the contrast against the first half. This decoding process reinforces message comprehension and helps them to correctly anticipate and applaud at the completion point of the contrast. In the example quoted above, the speaker micropaused for 0.2 seconds after the second word “spent,” at which point the audience began to applaud in anticipation of the completion point of the contrast, and applauded more excitedly upon hearing “. . . on the munitions of peace.” The applause continued and lasted for 9.2 long seconds.

A list is usually made up of a series of three parallel words, phrases or clauses. “Government of the people, by the people, for the people” is a fine example, as is Obama’s “It’s been a long time coming, but tonight, because of what we did on this day , in this election , at this defining moment , change has come to America!” (italics added) The three parts in the list echo one another, step up the argument and its corresponding excitement in the audience as they move from one part to the next. The third part projects a completion point to cue the audience to get themselves ready to display their support via applause, cheers, and so forth. In a real conversation this juncture is called a “transition-relevance place,” at which point a conversational partner (hearer) may take up a turn to speak. A skilful orator will micropause at that juncture to create a conversational space for the audience to take up their turn in applauding and cheering as a group.

As illustrated by the two examples above, speaker and audience collaborate to transform an otherwise monological speech into a quasiconversation, turning a passive audience into an active supportive “conversational” partner who, by their synchronized responses, reduces the psychological separation from the speaker and emboldens the latter’s self-confidence. Through such enjoyable and emotional participation collectively, an audience made up of formerly unconnected individuals with no strong common group identity may henceforth begin to feel “we are all one.” According to social identity theory and related theories (van Zomeren et al., 2008 ), the emergent group identity, politicized in the process, will in turn provide a social psychological base for collective social action. This process of identity making in the audience is further strengthened by the speaker’s frequent use of “we” as a first person, plural personal pronoun.

Conversational Power

A conversation is a speech exchange system in which the length and order of speaking turns have not been preassigned but require coordination on an utterance-by-utterance basis between two or more individuals. It differs from other speech exchange systems in which speaking turns have been preassigned and/or monitored by a third party, for example, job interviews and debate contests. Turn-taking, because of its centrality to conversations and the important theoretical issues that it raises for social coordination and implicit conversational conventions, has been the subject of extensive research and theorizing (Goodwin & Heritage, 1990 ; Grice, 1975 ; Sacks et al., 1974 ). Success at turn-taking is a key part of the conversational process leading to influence. A person who cannot do this is in no position to influence others in and through conversations, which are probably the most common and ubiquitous form of human social interaction. Below we discuss studies of conversational power based on conversational turns and applied to leader emergence in group and intergroup settings. These studies, as they unfold, link conversation analysis with social identity theory and expectation states theory (Berger et al., 1974 ).

A conversational turn in hand allows the speaker to influence others in two important ways. First, through current-speaker-selects-next the speaker can influence who will speak next and, indirectly, increases the probability that he or she will regain the turn after the next. A common method for selecting the next speaker is through tag questions. The current speaker (A) may direct a tag question such as “Ya know?” or “Don’t you agree?” to a particular hearer (B), which carries the illocutionary force of selecting the addressee to be the next speaker and, simultaneously, restraining others from self-selecting. The A 1 B 1 sequence of exchange has been found to have a high probability of extending into A 1 B 1 A 2 in the next round of exchange, followed by its continuation in the form of A 1 B 1 A 2 B 2 . For example, in a six-member group, the A 1 B 1 →A 1 B 1 A 2 sequence of exchange has more than 50% chance of extending to the A 1 B 1 A 2 B 2 sequence, which is well above chance level, considering that there are four other hearers who could intrude at either the A 2 or B 2 slot of turn (Stasser & Taylor, 1991 ). Thus speakership not only offers the current speaker the power to select the next speaker twice, but also to indirectly regain a turn.

Second, a turn in hand provides the speaker with an opportunity to exercise topic control. He or she can exercise non-decision-making power by changing an unfavorable or embarrassing topic to a safer one, thereby silencing or preventing it from reaching the “floor.” Conversely, he or she can exercise decision-making power by continuing or raising a topic that is favorable to self. Or the speaker can move on to talk about an innocuous topic to ease tension in the group.

Bales ( 1950 ) has studied leader emergence in groups made up of unacquainted individuals in situations where they have to bid or compete for speaking turns. Results show that individuals who talk the most have a much better chance of becoming leaders. Depending on the social orientations of their talk, they would be recognized as a task or relational leader. Subsequent research on leader emergence has shown that an even better behavioral predictor than volume of talk is the number of speaking turns. An obvious reason for this is that the volume of talk depends on the number of turns—it usually accumulates across turns, rather than being the result of a single extraordinary long turn of talk. Another reason is that more turns afford the speaker more opportunities to realize the powers of turns that have been explicated above. Group members who become leaders are the ones who can penetrate the complex, on-line conversational system to obtain a disproportionately large number of speaking turns by perfect timing at “transition-relevance places” to self-select as the next speaker or, paradoxical as it may seem, constructive interruptions (Ng et al., 1995 ).

More recent research has extended the experimental study of group leadership to intergroup contexts, where members belonging to two groups who hold opposing stances on a social or political issue interact within and also between groups. The results showed, first, that speaking turns remain important in leader emergence, but the intergroup context now generates social identity and self-categorization processes that selectively privilege particular forms of speech. What potential leaders say, and not only how many speaking turns they have gained, becomes crucial in conveying to group members that they are prototypical members of their group. Prototypical communication is enacted by adopting an accent, choosing code words, and speaking in a tone that characterize the in-group; above all, it is enacted through the content of utterances to represent or exemplify the in-group position. Such prototypical utterances that are directed successfully at the out-group correlate strongly with leader emergence (Reid & Ng, 2000 ). These out-group-directed prototypical utterances project an in-group identity that is psychologically distinctive from the out-group for in-group members to feel proud of and to rally together when debating with the out-group.

Building on these experimental results Reid and Ng ( 2003 ) developed a social identity theory of leadership to account for the emergence and maintenance of intergroup leadership, grounding it in case studies of the intergroup communication strategies that brought Ariel Sharon and John Howard to power in Israel and Australia, respectively. In a later development, the social identity account was fused with expectation states theory to explain how group processes collectively shape the behavior of in-group members to augment the prototypical communication behavior of the emergent leader (Reid & Ng, 2006 ). Specifically, when conversational influence gained through prototypical utterances culminates to form an incipient power hierarchy, group members develop expectations of who is and will be leading the group. Acting on these tacit expectations they collectively coordinate the behavior of each other to conform with the expectations by granting incipient leaders more speaking turns and supporting them with positive audience responses. In this way, group members collectively amplify the influence of incipient leaders and jointly propel them to leadership roles (see also Correll & Ridgeway, 2006 ). In short, the emergence of intergroup leaders is a joint process of what they do individually and what group members do collectively, enabled by speaking turns and mediated by social identity and expectation states processes. In a similar vein, Hogg ( 2014 ) has developed a social identity account of leadership in intergroup settings.

Narrative Power

Narratives and stories are closely related and are sometimes used interchangeably. However, it is useful to distinguish a narrative from a story and from other related terms such as discourse and frames. A story is a sequence of related events in the past recounted for rhetorical or ideological purposes, whereas a narrative is a coherent system of interrelated and sequentially organized stories formed by incorporating new stories and relating them to others so as to provide an ongoing basis for interpreting events, envisioning an ideal future, and motivating and justifying collective actions (Halverson et al., 2011 ). The temporal dimension and sense of movement in a narrative also distinguish it from discourse and frames. According to Miskimmon, O’Loughlin, and Roselle ( 2013 ), discourses are the raw material of communication that actors plot into a narrative, and frames are the acts of selecting and highlighting some events or issues to promote a particular interpretation, evaluation, and solution. Both discourse and frame lack the temporal and causal transformation of a narrative.

Pitching narratives at the suprastory level and stressing their temporal and transformational movements allows researchers to take a structurally more systemic and temporally more expansive view than traditional research on propaganda wars between nations, religions, or political systems (Halverson et al., 2011 ; Miskimmon et al., 2013 ). Schmid ( 2014 ) has provided an analysis of al-Qaeda’s “compelling narrative that authorizes its strategy, justifies its violent tactics, propagates its ideology and wins new recruits.” According to this analysis, the chief message of the narrative is “the West is at war with Islam,” a strategic communication that is fundamentally intergroup in both structure and content. The intergroup structure of al-Qaeda narrative includes the rhetorical constructions that there are a group grievance inflicted on Muslims by a Zionist–Christian alliance, a vision of the good society (under the Caliphate and sharia), and a path from grievance to the realization of the vision led by al-Qaeda in a violent jihad to eradicate Western influence in the Muslim world. The al-Qaeda narrative draws support not only from traditional Arab and Muslim cultural narratives interpreted to justify its unorthodox means (such as attacks against women and children), but also from pre-existing anti-Semitism and anti-Americanism propagated by some Arab governments, Soviet Cold War propaganda, anti-Western sermons by Muslim clerics, and the Israeli government’s treatment of Palestinians. It is deeply embedded in culture and history, and has reached out to numerous Muslims who have emigrated to the West.

The intergroup content of al-Qaeda narrative was shown in a computer-aided content analysis of 18 representative transcripts of propaganda speeches released between 2006–2011 by al-Qaeda leaders, totaling over 66,000 words (Cohen et al., 2016 ). As part of the study, an “Ideology Extraction using Linguistic Extremization” (IELEX) categorization scheme was developed for mapping the content of the corpus, which revealed 19 IELEX rhetorical categories referring to either the out-group/enemy or the in-group/enemy victims. The out-group/enemy was represented by four categories such as “The enemy is extremely negative (bloodthirsty, vengeful, brainwashed, etc.)”; whereas the in-group/enemy victims were represented by more categories such as “we are entirely innocent/good/virtuous.” The content of polarized intergroup stereotypes, demonizing “them” and glorifying “us,” echoes other similar findings (Smith et al., 2008 ), as well as the general finding of intergroup stereotyping in social psychology (Yzerbyt, 2016 ).

The success of the al-Qaeda narrative has alarmed various international agencies, individual governments, think tanks, and religious groups to spend huge sums of money on developing counternarratives that are, according to Schmid ( 2014 ), largely feeble. The so-called “global war on terror” has failed in its effort to construct effective counternarratives although al-Qaeda’s finance, personnel, and infrastructure have been much weakened. Ironically, it has developed into a narrative of its own, not so much for countering external extremism, but for promoting and justifying internal nationalistic extremist policies and influencing national elections. This reactive coradicalization phenomenon is spreading (Mink, 2015 ; Pratt, 2015 ; Reicher & Haslam, 2016 ).

Discussion and Future Directions

This chapter provides a systematic framework for understanding five language–power relationships, namely, language reveals power, reflects power, maintains existing dominance, unites and divides a nation, and creates influence. The first two relationships are derived from the power behind language and the last three from the power of language. Collectively they provide a relatively comprehensible framework for understanding the relationships between language and power, and not simply for understanding language alone or power alone separated from one another. The language–power relationships are dynamically interrelated, one influencing the other, and each can draw from an array of the cognitive, communicative, social, and identity functions of language. The framework is applicable to both interpersonal and intergroup contexts of communication, although for present purposes the latter has been highlighted. Among the substantive issues discussed in this chapter, English as a global language, oratorical and narrative power, and intergroup leadership stand out as particularly important for political and theoretical reasons.

In closing, we note some of the gaps that need to be filled and directions for further research. When discussing the powers of language to maintain and reflect existing dominance, we have omitted the countervailing power of language to resist or subvert existing dominance and, importantly, to create social change for the collective good. Furthermore, in this age of globalization and its discontents, English as a global language will increasingly be resented for its excessive unaccommodating power despite tangible lingua franca English benefits, and challenged by the expanding ethnolinguistic vitality of peoples who speak Arabic, Chinese, or Spanish. Internet communication is no longer predominantly in English, but is rapidly diversifying to become the modern Tower of Babel. And yet we have barely scratched the surface of these issues. Other glaring gaps include the omission of media discourse and recent developments in Corpus-based Critical Discourse Analysis (Loring, 2016 ), as well as the lack of reference to languages other than English that may cast one or more of the language–power relationships in a different light.

One of the main themes of this chapter—that the diverse language–power relationships are dynamically interrelated—clearly points to the need for greater theoretical fertilization across cognate disciplines. Our discussion of the three powers of language (boxes 3–5 in Figure 1 ) clearly points in this direction, most notably in the case of the powers of language to create influence through single words, oratories, conversations, and narratives, but much more needs to be done. The social identity approach will continue to serve as a meta theory of intergroup communication. To the extent that intergroup communication takes place in an existing power relation and that the changes that it seeks are not simply a more positive or psychologically distinctive social identity but greater group power and a more powerful social identity, the social identity approach has to incorporate power in its application to intergroup communication.

Further Reading

• Austin, J. L. (1975). How to do things with words . Oxford: Oxford University Press.
• Billig, M. (1991). Ideology and opinions: Studies in rhetorical psychology . Newbury Park, CA: SAGE.
• Crystal, D. (2012). English as a global language , 2d ed. Cambridge, U.K.: Cambridge University Press.
• Culpeper, J. (2011). Impoliteness . New York: John Wiley.
• Holtgraves, T. M. (2010). Social psychology and language: Words, utterances, and conversations. In S. Fiske , D. Gilbert , & G. Lindzey (Eds.), Handbook of social psychology (5th ed., pp. 1386–1422). New York: John Wiley.
• Mumby, D. K. (Ed.). (1993). Narrative and social control: Critical perspectives (Vol. 21). Newbury Park, CA: SAGE.
• Ng, S. H. , & Bradac, J. J. (1993). Power in language: Verbal communication and social influence . Newbury Park, CA: SAGE. Retrieved from http://dx.doi.org/10.4135/9781412994088.n202 .
• Abrams, D. , & Hogg, M. A. (2004). Metatheory: Lessons from social identity research. Personality and Social Psychology Review , 8 , 98–106.
• Bales, R. F. (1950). Interaction process analysis: A method for the study of small groups . Oxford: Addison-Wesley.
• Benedek, M. , Beaty, R. , Jauk, E. , Koschutnig, K. , Fink, A. , Silvia, P. J. , . . . & Neubauer, A. C. (2014). Creating metaphors: The neural basis of figurative language production. NeuroImage , 90 , 99–106.
• Berger, J. , Conner, T. L. , & Fisek, M. H. (Eds.). (1974). Expectation states theory: A theoretical research program . Cambridge, MA: Winthrop.
• Bolton, K. (2008). World Englishes today. In B. B. Kachru , Y. Kachru , & C. L. Nelson (Eds.), The handbook of world Englishes (pp. 240–269). Oxford: Blackwell.
• Bourhis, R. Y. , Giles, H. , & Rosenthal, D. (1981). Notes on the construction of a “Subjective vitality questionnaire” for ethnolinguistic groups. Journal of Multilingual and Multicultural Development , 2 , 145–155.
• British Council . (n.d.). Retrieved from http://www.britishcouncil.org/learning-faq-the-english-language.htm .
• Brosch, C. (2015). On the conceptual history of the term Lingua Franca . Apples . Journal of Applied Language Studies , 9 (1), 71–85.
• Calvet, J. (1998). Language wars and linguistic politics . Oxford: Oxford University Press.
• Cenoz, J. , & Gorter, D. (2006). Linguistic landscape and minority languages. International Journal of Multilingualism , 3 , 67–80.
• Cohen, S. J. , Kruglanski, A. , Gelfand, M. J. , Webber, D. , & Gunaratna, R. (2016). Al-Qaeda’s propaganda decoded: A psycholinguistic system for detecting variations in terrorism ideology . Terrorism and Political Violence , 1–30.
• Correll, S. J. , & Ridgeway, C. L. (2006). Expectation states theory . In L. DeLamater (Ed.), Handbook of social psychology (pp. 29–51). Hoboken, NJ: Springer.
• Danet, B. (1980). Language in the legal process. Law and Society Review , 14 , 445–564.
• DeVotta, N. (2004). Blowback: Linguistic nationalism, institutional decay, and ethnic conflict in Sri Lanka . Stanford, CA: Stanford University Press.
• Dragojevic, M. , & Giles, H. (2014). Language and interpersonal communication: Their intergroup dynamics. In C. R. Berger (Ed.), Handbook of interpersonal communication (pp. 29–51). Berlin: De Gruyter.
• Emerson, R. M. (1962). Power–Dependence Relations. American Sociological Review , 27 , 31–41.
• Fairclough, N. L. (1989). Language and power . London: Longman.
• Foucault, M. (1979). The history of sexuality volume 1: An introduction . London: Allen Lane.
• Friginal, E. (2007). Outsourced call centers and English in the Philippines. World Englishes , 26 , 331–345.
• Giles, H. (Ed.) (2012). The handbook of intergroup communication . New York: Routledge.
• Goodwin, C. , & Heritage, J. (1990). Conversation analysis. Annual review of anthropology , 19 , 283–307.
• Grice, H. P. (1975). Logic and conversation. In P. Cole & J. Morgan (Eds.), Syntax and semantics (pp. 41–58). New York: Academic Press.
• Halverson, J. R. , Goodall H. L., Jr. , & Corman, S. R. (2011). Master narratives of Islamist extremism . New York: Palgrave Macmillan.
• Hartshorne, J. K. , & Snedeker, J. (2013). Verb argument structure predicts implicit causality: The advantages of finer-grained semantics. Language and Cognitive Processes , 28 , 1474–1508.
• Harwood, J. , Giles, H. , & Bourhis, R. Y. (1994). The genesis of vitality theory: Historical patterns and discoursal dimensions. International Journal of the Sociology of Language , 108 , 167–206.
• Harwood, J. , Giles, H. , & Palomares, N. A. (2005). Intergroup theory and communication processes. In J. Harwood & H. Giles (Eds.), Intergroup communication: Multiple perspectives (pp. 1–20). New York: Peter Lang.
• Harwood, J. , & Roy, A. (2005). Social identity theory and mass communication research. In J. Harwood & H. Giles (Eds.), Intergroup communication: Multiple perspectives (pp. 189–212). New York: Peter Lang.
• Heritage, J. , & Greatbatch, D. (1986). Generating applause: A study of rhetoric and response at party political conferences. American Journal of Sociology , 92 , 110–157.
• Hogg, M. A. (2014). From uncertainty to extremism: Social categorization and identity processes. Current Directions in Psychological Science , 23 , 338–342.
• Holtgraves, T. M. (Ed.). (2014a). The Oxford handbook of language and social psychology . Oxford: Oxford University Press.
• Holtgraves, T. M. (2014b). Interpreting uncertainty terms. Journal of Personality and Social Psychology , 107 , 219–228.
• Jenkins, J. (2009). English as a lingua franca: interpretations and attitudes. World Englishes , 28 , 200–207.
• Jones, L. , & Watson, B. M. (2012). Developments in health communication in the 21st century. Journal of Language and Social Psychology , 31 , 415–436.
• Kachru, B. B. (1992). The other tongue: English across cultures . Urbana: University of Illinois Press.
• Landau, M. J. , Robinson, M. D. , & Meier, B. P. (Eds.). (2014). The power of metaphor: Examining its influence on social life . Washington, DC: American Psychological Association.
• Landry, R. , & Bourhis, R. Y. (1997). Linguistic landscape and ethnolinguistic vitality an empirical study. Journal of language and social psychology , 16 , 23–49.
• Lenski, G. (1966). Power and privilege: A theory of social stratification . New York: McGraw-Hill.
• Loftus, E. F. (1975). Leading questions and the eyewitness report. Cognitive Psychology , 7 , 560–572.
• Loring, A. (2016). Ideologies and collocations of “Citizenship” in media discourse: A corpus-based critical discourse analysis. In A. Loring & V. Ramanathan (Eds.), Language, immigration and naturalization: Legal and linguistic issues (chapter 9). Tonawanda, NY: Multilingual Matters.
• Lukes, S. (2005). Power: A radical view , 2d ed. New York: Palgrave.
• Maass, A. (1999). Linguistic intergroup bias: Stereotype perpetuation through language. Advances in experimental social psychology , 31 , 79–121.
• Marshal, N. , Faust, M. , Hendler, T. , & Jung-Beeman, M. (2007). An fMRI investigation of the neural correlates underlying the processing of novel metaphoric expressions. Brain and language , 100 , 115–126.
• Mertz, E. , Ford, W. K. , & Matoesian, G. (Eds.). (2016). Translating the social world for law: Linguistic tools for a new legal realism . New York: Oxford University Press.
• Mink, C. (2015). It’s about the group, not god: Social causes and cures for terrorism. Journal for Deradicalization , 5 , 63–91.
• Miskimmon, A. , O’Loughlin, B. , & Roselle, L. (2013). Strategic narratives: Communicating power and the New World Order . New York: Routledge.
• Ng, S. H. , Brooke, M. & Dunne, M. (1995). Interruptions and influence in discussion groups. Journal of Language & Social Psychology , 14 , 369–381.
• O’Barr, W. M. (1982). Linguistic evidence: Language, power, and strategy in the courtroom . London: Academic Press.
• Palomares, N. A. , Giles, H. , Soliz, J. , & Gallois, C. (2016). Intergroup accommodation, social categories, and identities. In H. Giles (Ed.), Communication accommodation theory: Negotiating personal relationships and social identities across contexts (pp. 123–151). Cambridge, U.K.: Cambridge University Press.
• Patten, A. (2006). The humanist roots of linguistic nationalism. History of Political Thought , 27 , 221–262.
• Phillipson, R. (2009). Linguistic imperialism continued . New York: Routledge.
• Pratt, D. (2015). Reactive co-radicalization: Religious extremism as mutual discontent. Journal for the Academic Study of Religion , 28 , 3–23.
• Raven, B. H. (2008). The bases of power and the power/interaction model of interpersonal influence. Analyses of Social Issues and Public Policy , 8 , 1–22.
• Reicher, S. D. , & Haslam, S. A. (2016). Fueling extremes. Scientific American Mind , 27 , 34–39.
• Reid, S. A. , Giles, H. , & Harwood, J. (2005). A self-categorization perspective on communication and intergroup relations. In J. Harwood & H. Giles (Eds.), Intergroup communication: Multiple perspectives (pp. 241–264). New York: Peter Lang.
• Reid, S. A. , & Ng, S. H. (2000). Conversation as a resource for influence: Evidence for prototypical arguments and social identification processes. European Journal of Social Psychology , 30 , 83–100.
• Reid, S. A. , & Ng, S. H. (2003). Identity, power, and strategic social categorisations: Theorising the language of leadership. In P. van Knippenberg & M. A. Hogg (Eds.), Leadership and power: Identity processes in groups and organizations (pp. 210–223). London: SAGE.
• Reid, S. A. , & Ng, S. H. (2006). The dynamics of intragroup differentiation in an intergroup social context. Human Communication Research , 32 , 504–525.
• Reisigl, M. , & Wodak, R. (2005). Discourse and discrimination: Rhetorics of racism and antisemitism . London: Routledge.
• Robinson, W. P. (1996). Deceit, delusion, and detection . Newbury Park, CA: SAGE.
• Rubini, M. , Moscatelli, S. , Albarello, F. , & Palmonari, A. (2007). Group power as a determinant of interdependence and intergroup discrimination. European Journal of Social Psychology , 37 (6), 1203–1221.
• Russell, B. (2004). Power: A new social analysis . Originally published in 1938. London: Routledge.
• Ryan, E. B. , Hummert, M. L. , & Boich, L. H. (1995). Communication predicaments of aging patronizing behavior toward older adults. Journal of Language and Social Psychology , 14 (1–2), 144–166.
• Sacks, H. , Schegloff, E. A. , & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. L anguage , 50 , 696–735.
• Sassenberg, K. , Ellemers, N. , Scheepers, D. , & Scholl, A. (2014). “Power corrupts” revisited: The role of construal of power as opportunity or responsibility. In J. -W. van Prooijen & P. A. M. van Lange (Eds.), Power, politics, and paranoia: Why people are suspicious of their leaders (pp. 73–87). Cambridge, U.K.: Cambridge University Press.
• Schmid, A. P. (2014). Al-Qaeda’s “single narrative” and attempts to develop counter-narratives: The state of knowledge . The Hague, The Netherlands: International Centre for Counter-Terrorism, 26. Available at https://www.icct.nl/download/file/A-Schmid-Al-Qaedas-Single-Narrative-January-2014.pdf .
• Semin, G. R. , & Fiedler, K. (1991). The linguistic category model, its bases, applications and range. In W. Stroebe & M. Hewstone (Eds.), European review of social psychology (Vol. 2, pp. 1–50). Chichester, U.K.: John Wiley.
• Smith, A. G. , Suedfeld, P. , Conway, L. G. , IIl, & Winter, D. G. (2008). The language of violence: Distinguishing terrorist from non-terrorist groups by thematic content analysis. Dynamics of Asymmetric Conflict , 1 (2), 142–163.
• Spender, D. (1998). Man made language , 4th ed. London: Pandora.
• Stasser, G. , & Taylor, L. (1991). Speaking turns in face-to-face discussions. Journal of Personality & Social Psychology , 60 , 675–684.
• Stolte, J. (1987). The formation of justice norms. American Sociological Review , 52 (6), 774–784.
• Sun, J. J. M. , Hu, P. , & Ng, S. H. (2016). Impact of English on education reforms in China: With reference to the learn-English movement, the internationalisation of universities and the English language requirement in college entrance examinations . Journal of Multilingual and Multicultural Development , 1–14 (Published online January 22, 2016).
• Tajfel, H. (1982). Social psychology of intergroup relations. Annual Review of Psychology , 33 , 1–39.
• Turner, J. C. (2005). Explaining the nature of power: A three—process theory. European Journal of Social Psychology , 35 , 1–22.
• Van Zomeren, M. , Postmes, T. , & Spears, R. (2008). Toward an integrative social identity model of collective action: A quantitative research synthesis of three socio-psychological perspectives. Psychological Bulletin , 134 (4), 504–535.
• Wakslak, C. J. , Smith, P. K. , & Han, A. (2014). Using abstract language signals power. Journal of Personality and Social Psychology , 107 (1), 41–55.
• Xiang, M. , & Kuperberg, A. (2014). Reversing expectations during discourse comprehension . Language, Cognition and Neuroscience , 30 , 648–672.
• Yzerbyt, V. (2016). Intergroup stereotyping. Current Opinion in Psychology , 11 , 90–95.

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The power of language: How words shape people, culture

Speaking, writing and reading are integral to everyday life, where language is the primary tool for expression and communication. Studying how people use language – what words and phrases they unconsciously choose and combine – can help us better understand ourselves and why we behave the way we do.

Linguistics scholars seek to determine what is unique and universal about the language we use, how it is acquired and the ways it changes over time. They consider language as a cultural, social and psychological phenomenon.

“Understanding why and how languages differ tells about the range of what is human,” said Dan Jurafsky , the Jackson Eli Reynolds Professor in Humanities and chair of the Department of Linguistics in the School of Humanities and Sciences at Stanford . “Discovering what’s universal about languages can help us understand the core of our humanity.”

The stories below represent some of the ways linguists have investigated many aspects of language, including its semantics and syntax, phonetics and phonology, and its social, psychological and computational aspects.

Understanding stereotypes

Stanford linguists and psychologists study how language is interpreted by people. Even the slightest differences in language use can correspond with biased beliefs of the speakers, according to research.

One study showed that a relatively harmless sentence, such as “girls are as good as boys at math,” can subtly perpetuate sexist stereotypes. Because of the statement’s grammatical structure, it implies that being good at math is more common or natural for boys than girls, the researchers said.

Language can play a big role in how we and others perceive the world, and linguists work to discover what words and phrases can influence us, unknowingly.

How well-meaning statements can spread stereotypes unintentionally

New Stanford research shows that sentences that frame one gender as the standard for the other can unintentionally perpetuate biases.

Algorithms reveal changes in stereotypes

New Stanford research shows that, over the past century, linguistic changes in gender and ethnic stereotypes correlated with major social movements and demographic changes in the U.S. Census data.

Exploring what an interruption is in conversation

Stanford doctoral candidate Katherine Hilton found that people perceive interruptions in conversation differently, and those perceptions differ depending on the listener’s own conversational style as well as gender.

Cops speak less respectfully to black community members

Professors Jennifer Eberhardt and Dan Jurafsky, along with other Stanford researchers, detected racial disparities in police officers’ speech after analyzing more than 100 hours of body camera footage from Oakland Police.

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People speak roughly 7,000 languages worldwide. Although there is a lot in common among languages, each one is unique, both in its structure and in the way it reflects the culture of the people who speak it.

Jurafsky said it’s important to study languages other than our own and how they develop over time because it can help scholars understand what lies at the foundation of humans’ unique way of communicating with one another.

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Stanford PhD student documents indigenous language of Papua New Guinea

Fifth-year PhD student Kate Lindsey recently returned to the United States after a year of documenting an obscure language indigenous to the South Pacific nation.

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In a research project spanning eight countries, two Stanford students search for Esperanto, a constructed language, against the backdrop of European populism.

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A computer scientist discusses the evolution of computational linguistics and where it’s headed next.

Stanford research explores novel perspectives on the evolution of Spanish

Using digital tools and literature to explore the evolution of the Spanish language, Stanford researcher Cuauhtémoc García-García reveals a new historical perspective on linguistic changes in Latin America and Spain.

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A Stanford senior studied a group of bilingual children at a Spanish immersion preschool in Texas to understand how they distinguished between their two languages.

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Stanford linguist Dan Jurafsky and colleagues have found that products in Japan sell better if their advertising includes polite language and words that invoke cultural traditions or authority.

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By examining conversations of elderly Japanese women, linguist Yoshiko Matsumoto uncovers language techniques that help people move past traumatic events and regain a sense of normalcy.

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A psycholinguistic study of intergroup bias and its cultural propagation

• Daniel Schmidtke
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The language network as a natural kind within the broader landscape of the human brain

Many brain areas support complex language processing behaviours. In this Review, Fedorenko et al. disentangle the ‘core’ language system as functionally distinct from the perceptual and motor brain areas and knowledge and reasoning systems it closely interacts with during language comprehension and production.

• Evelina Fedorenko
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Repeatedly experiencing the McGurk effect induces long-lasting changes in auditory speech perception

In the McGurk effect, seeing the talker’s face changes perception of auditory speech. Repeatedly experiencing the effect produces long-lasting changes in auditory perception, so that the McGurk fusion percept is evoked even without seeing the face.

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Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns

Here, using neural activity patterns in the inferior frontal gyrus and large language modeling embeddings, the authors provide evidence for a common neural code for language processing.

• Ariel Goldstein
• Avigail Grinstein-Dabush

Electrophysiological signatures of spelling sensitivity development from primary school age to adulthood

• Ekaterina Larionova
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The neural basis of naturalistic semantic and social cognition

• Melissa Thye
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Three reasons why ai doesn’t model human language.

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Communication barriers for a Deaf PhD student meant risking burnout

Megan Majocha is gearing up to complete her PhD. But developing a sign-language lexicon to help her succeed took an immense toll during her scientific research.

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This AI learnt language by seeing the world through a baby’s eyes

A neural network that taught itself to recognize objects using the filmed experiences of a single infant could offer new insights into how humans learn.

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Genomic data from ancient humans who lived up to 7,400 years ago, sampled from across California and Mexico, unveil patterns of migration that could explain how some Indigenous languages spread in parts of North America.

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The littlest linguists: new research on language development.

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How do children learn language, and how is language related to other cognitive and social skills? For decades, the specialized field of developmental psycholinguistics has studied how children acquire language—or multiple languages—taking into account biological, neurological, and social factors that influence linguistic developments and, in turn, can play a role in how children learn and socialize. Here’s a look at recent research (2020–2021) on language development published in Psychological Science . 

Preverbal Infants Discover Statistical Word Patterns at Similar Rates as Adults: Evidence From Neural Entrainment

Dawoon Choi, Laura J. Batterink, Alexis K. Black, Ken A. Paller, and Janet F. Werker (2020)

One of the first challenges faced by infants during language acquisition is identifying word boundaries in continuous speech. This neurological research suggests that even preverbal infants can learn statistical patterns in language, indicating that they may have the ability to segment words within continuous speech.

Using electroencephalogram measures to track infants’ ability to segment words, Choi and colleagues found that 6-month-olds’ neural processing increasingly synchronized with the newly learned words embedded in speech over the learning period in one session in the laboratory. Specifically, patterns of electrical activity in their brains increasingly aligned with sensory regularities associated with word boundaries. This synchronization was comparable to that seen among adults and predicted future ability to discriminate words.

These findings indicate that infants and adults may follow similar learning trajectories when tracking probabilities in speech, with both groups showing a logarithmic (rather than linear) increase in the synchronization of neural processing with frequent words. Moreover, speech segmentation appears to use neural mechanisms that emerge early in life and are maintained throughout adulthood.

Parents Fine-Tune Their Speech to Children’s Vocabulary Knowledge

Ashley Leung, Alexandra Tunkel, and Daniel Yurovsky (2021)

Children can acquire language rapidly, possibly because their caregivers use language in ways that support such development. Specifically, caregivers’ language is often fine-tuned to children’s current linguistic knowledge and vocabulary, providing an optimal level of complexity to support language learning. In their new research, Leung and colleagues add to the body of knowledge involving how caregivers foster children’s language acquisition.

The researchers asked individual parents to play a game with their child (age 2–2.5 years) in which they guided their child to select a target animal from a set. Without prompting, the parents provided more informative references for animals they thought their children did not know. For example, if a parent thought their child did not know the word “leopard,” they might use adjectives (“the spotted, yellow leopard”) or comparisons (“the one like a cat”). This indicates that parents adjust their references to account for their children’s language knowledge and vocabulary—not in a simplifying way but in a way that could increase the children’s vocabulary. Parents also appeared to learn about their children’s knowledge throughout the game and to adjust their references accordingly.

Infant and Adult Brains Are Coupled to the Dynamics of Natural Communication

Elise A. Piazza, Liat Hasenfratz, Uri Hasson, and Casey Lew-Williams (2020)

This research tracked real-time brain activation during infant–adult interactions, providing an innovative measure of social interaction at an early age. When communicating with infants, adults appear to be sensitive to subtle cues that can modify their brain responses and behaviors to improve alignment with, and maximize information transfer to, the infants.

Piazza and colleagues used functional near-infrared spectroscopy—a noninvasive measure of blood oxygenation resulting from neural activity that is minimally affected by movements and thus allows participants to freely interact and move—to measure the brain activation of infants (9–15 months old) and adults while they communicated and played with each other. An adult experimenter either engaged directly with an infant by playing with toys, singing nursery rhymes, and reading a story or performed those same tasks while turned away from the child and toward another adult in the room.

Results indicated that when the adult interacted with the child (but not with the other adult), the activations of many prefrontal cortex (PFC) channels and some parietal channels were intercorrelated, indicating neural coupling of the adult’s and child’s brains. Both infant and adult PFC activation preceded moments of mutual gaze and increased before the infant smiled, with the infant’s PFC response preceding the adult’s. Infant PFC activity also preceded an increase in the pitch variability of the adult’s speech, although no changes occurred in the adult’s PFC, indicating that the adult’s speech influenced the infant but probably did not influence neural coupling between the child and the adult.

Theory-of-Mind Development in Young Deaf Children With Early Hearing Provisions

Chi-Lin Yu, Christopher M. Stanzione, Henry M. Wellman, and Amy R. Lederberg (2020)

Language and communication are important for social and cognitive development. Although deaf and hard-of-hearing (DHH) children born to deaf parents can communicate with their caregivers using sign language, most DHH children are born to hearing parents who do not have experience with sign language. These children may have difficulty with early communication and experience developmental delays. For instance, the development of theory of mind—the understanding of others’ mental states—is usually delayed in DHH children born to hearing parents.

Yu and colleagues studied how providing DHH children with hearing devices early in life (before 2 years of age) might enrich their early communication experiences and benefit their language development, supporting the typical development of other capabilities—in particular, theory of mind. The researchers show that 3- to 6-year-old DHH children who began using cochlear implants or hearing aids earlier had more advanced language abilities, leading to better theory-of-mind growth, than children who started using hearing provisions later. These findings highlight the relationships among hearing, language, and theory of mind.

The Bilingual Advantage in Children’s Executive Functioning Is Not Related to Language Status: A Meta-Analytic Review

Cassandra J. Lowe, Isu Cho, Samantha F. Goldsmith, and J. Bruce Morton (2021)

Acommon idea is that bilingual children, who grow up speaking two languages fluently, perform better than monolingual children in diverse executive-functioning domains (e.g., attention, working memory, decision making). This meta-analysis calls that idea into question.

Lowe and colleagues synthesized data from studies that compared the performance of monolingual and bilingual participants between the ages of 3 and 17 years in executive-functioning domains (1,194 effect sizes). They found only a small effect of bilingualism on participants’ executive functioning, which was largely explained by factors such as publication bias. After accounting for these factors, bilingualism had no distinguishable effect. The results of this large meta-analysis thus suggest that bilingual and monolingual children tend to perform at the same level in executive-functioning tasks. Bilingualism does not appear to boost performance in executive functions that serve learning, thinking, reasoning, or problem solving.

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Teaching: Ethical Research to Help Romania’s Abandoned Children 

An early intervention experiment in Bucharest can introduce students to the importance of responsive caregiving during human development.

Silver Linings in the Demographic Revolution 

Podcast: In her final column as APS President, Alison Gopnik makes the case for more effectively and creatively caring for vulnerable humans at either end of life.

Communicating Psychological Science: The Lifelong Consequences of Early Language Skills

“When families are informed about the importance of conversational interaction and are provided training, they become active communicators and directly contribute to reducing the word gap (Leung et al., 2020).”

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Medieval Institute Publications

The labor of research in modern academia.

I am happy to see The Language of Heresy in Late Medieval English Literature released—and I appreciate so much the work Medieval Institute Publications and De Gruyter have done on this book. I have nothing but praise for the editors and publicists, proofreaders and designers.

But this text also represents, for me, a great amount of anxiety and frustration, of work completed during mental unhealth and job insecurity. Over the years since my doctoral graduation, I could not shake, as I am sure is the case for others, that I would not be a real scholar until I published a monograph.

I drafted The Language of Heresy over three to four years. If you only count the years in which a file dedicated to this project existed on my computer. If you don’t count the 2-3 years spent writing the dissertation out of which the book, much changed, came; the five years or so in which I cut out and revised articles from the dissertation, preparing myself for a refined project; or the year spent navigating the job market between positions, all writing energy spent drafting and redrafting cover letters (a hateful genre).

 When I went on the job market during the last year of my Ph.D., Medieval English job openings were few and far between. By which I mean there was a  relatively high number of postings.  But those were twenty openings in an international market to be split between every graduate, public, private, and ivy alike. Late in the s eason, I landed a full-time gig, teaching a 4/4 load, at a small institution that had recently abandoned tenure and was on the verge of being fully collapsed into a large online university/business.

When the collapse came, most full-time teaching jobs were eliminated. As a new faculty member, I was much luckier than my colleagues—and I retained a job in an academic support capacity. But I was unhappy (and insecure) in my new position and so returned to the job market. The combined forces of career ennui and new job docs hardly incentivized my research.

After a year in this limbo, I accepted a full-time tenure-track position, still with a 4/4 load, as a generalist in the SUNY system. Now tenured, I am the lone medievalist at a college of technology in rural New York and, on rare occasion, can offer a Brit Lit survey. Usually, I teach a combination of Composition and Intro to Lit courses.

This is not intended to be a sob story. In almost every way, it is a privileged narrative. And I feel lucky to have my job and to work with my colleagues and students. But I did want to take a moment to acknowledge how labor-intensive a task it is to complete a book in all but the most perfect of circumstances and research positions.

I have never had a sabbatical. Any course releases come with increased service that is often more work than teaching. I grade papers and homework for 4 classes, for 60-80 students, on a biweekly schedule; I am in the classroom every day most semesters. Our library has no medieval resources and access to few databases; there is almost no book or article that I do not have to order through ILL or from another library, adding weeks’ worth of waiting to any research timeline. I rely, sometimes, on Google Books to try and find—frantically—a sentence I need in a book that I have already returned (pro tip: sometimes an incognito browser will let you view a different selection of pages).

Now add onto this the insecurity of an adjunct. Or an even higher teaching load. This is what academia, in 2024, looks like. And it doesn’t look set to change in the near future.

So, here’s my point. As we medievalists, and academics more generally, strive to make our field more inclusive and more equitable, we should not forget to simultaneously adjust our expectations of labor and research. It is, after all, a western and patriarchal institution that has set the monograph as a minimum achievement among research expectations, a lingering reminder of a fictionalized heyday of academia where the elbow-patched and bespectacled professor had lifetimes and libraries to help him write.

Let us celebrate the articles and the teaching accomplishments, the public scholarship and activism, and value them as they deserve to be valued, to value not the amount of pages but their contribution.

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Researching language learning motivation: A concise guide

Language learning motivation has long been embraced by researchers as a crucial concept in second language acquisition and has arrested the attention of scholars worldwide for over six decades (Al-Hoorie, 2017 ; Ushioda, 2019 ). Among the explorations of what motivation is and how to make the best of it for language learning, scholars have centered on Gardner's socio-educational model (Gardner, 2010 ) and the L2 Motivational Self System (Dörnyei, 2005 ). The former features social–psychological views toward social group motivations and contextual variables (Dörnyei and Ryan, 2015 ), while the latter represents a cognitive-situated lens to learner-based classroom-oriented research. However, as the field starts to shift toward sociodynamic perspectives (Dörnyei and Ryan, 2015 ), what needs to be investigated is how the limitations described in Al-Hoorie's ( 2017 ) study have been addressed, and what the recent developments and future directions are. The gap has been well-filled by the book under review, Researching Language Learning Motivation: A Concise Guide (Al-Hoorie and Szabó, 2022 ), which serves as a timely, accessible, and detailed extension on the forward-pointing survey of emerging themes illustrated by Al-Hoorie ( 2017 ), and offers guidance for future research directions.

This volume contains an introduction, an afterword, and 21 chapters organized under five overarching themes. Part 1 (Chapters 1–3) addresses general reflections on the field of motivation. Ema Ushioda devotes Chapter 1 to the pedagogical implications of “motivational teaching practice.” In Chapter 2, Matthew E. Poehner adopts the notion of “perezhivanie” from sociocultural theory to reflect on the dialectical views of motivation. Similarly, Ofelia García in Chapter 3 goes beyond the psychological paradigm and advocates a sociological paradigm to unpack how language scholars have been accustomed to viewing language learners from a deficit view in terms of native speaker-like language abilities and proficiency, and proposes a translanguaging perspective to reconsider language learners in terms of their meaning-making repertoire.

Part 2 (Chapters 4–7) focuses on the critical issues of language engagement. Sarah Mercer illustrates a tripartite model of classroom engagement in great length to underline the complexities in language classrooms and ends the chapter by outlining three promising directions for future research. In Chapter 5, Phil Hiver unpacks the multi-dimensionality of engagement and illustrates teachers' adaptive expertise in managing the unpredictability in instructional settings. In Chapter 6, Alastair Henry examines the nature of language learners' goals and illustrates how quality goals can sustain motivation. Chapter 7 provides a concise overview of self-determination theory, together with its six interdependent mini-theories, and elaborates on how it contributes to motivation and engagement through dialogic interactions between teachers and students.

Part 3 (Chapters 8–11) explores motivation from “selves approaches.” Chapter 8 by Peter D. MacIntyre presents an overview of the L2 Motivational Self System and explains how it evolves from the self-theory. In Chapter 9, Mostafa Papi endeavors to test the L2 Motivational Self System by highlighting how it can be adapted to empower language teaching practice in instructional settings. In Chapter 10, Amy Thompson focuses on her experience of learning a Language other than English (LOTE) in rural America and explains how the projection of an ideal L2 self helps to maximize learning potential, regardless of limited language learning resources. In a similar vein, Chapter 11 examines the influence of technology use on L2 selves and analyzes how this technology-empowered digital visualization of L2 selves contributes to learning engagement and second language acquisition.

Part 4 (Chapters 12–15) explores studies that link motivation with emotions and affect. Through a combination of personal and historical perspectives, Jean-Marc Dewaele traces in Chapter 12 the origin and development of emotion research in the field of second language acquisition. Chapter 13, centering around emotional engagement and emotional distractions in foreign language classrooms, unveils the causes of silent L2 learners' social anxiety in light of cognitive-behavioral theory (CBT) and proposes CBT-based classroom activities to promote students' emotional engagement. Chapter 14 investigates motivation contagion through an examination of the dynamic interplay of language teachers' and learners' motivation and explains how language teachers and learners can capitalize on it and maximize its reciprocal impact. Following a detailed introduction about directed motivational currents (DMCs), emotional contagion, and group emotion, the author in Chapter 15 discusses how group-level effects can help to understand DMCs and long-term motivations.

Part 5 (Chapters 16–21) extends the exploration of motivation into newly emerging topics in the field. In Chapter 16, Ali H. Al-Hoorie and Phil Hiver highlight the need to upgrade and diversify the current toolbox of methods for conducting complex dynamic systems theory (CDST) research. Based on a longitudinal study of nine Indonesian learners' motivation in learning English, Chapter 17 delves into “elite multilingualism” through notions of desires, identities, and power from a critical discourse analysis perspective. Chapter 18 expounds on the linguistic and cultural diversities in a globalized world of ever-frequent migration and mobility and ventures to investigate motivation in multi-ethnic settings. In Chapter 19, Zana Ibrahim elaborates on the liberating effect of the concept of English as a Lingua Franca (ELF) on second language learners. Based on his rich experience in applied linguistics and neuroscience, Robert S. Murphy dedicates Chapter 20 to seven neuroELT maxims that can lead to success in motivating students in language classrooms. Finally, Chapter 21, by integrating anthropological, affective, and social perspectives, sheds light on the effects of group dynamics on learners' wellbeing and language learning motivation.

Overall, this volume, with its chapters contributed by established scholars and active researchers worldwide, features succinct and accessible overviews for newcomers to the field of language learning motivation. For both students and novice researchers, the current volume could be a good start to embark on the journey of language learning motivation, as its comprehensive and up-to-date overviews can offer the readership an encompassing view of the major approaches and topics in researching language learning motivation. In view of the approaches, while the foundational social–psychological approach featuring integrative motivation and instrumental motivation can be found mentioned in various chapters, its applicability is being challenged in various language learning scenarios, where English as a Lingua Franca (Chapter 19) or multilingualism (Chapter 18) is widely accepted. Under both circumstances, the monolingual bias has to be abandoned and the integrative motivation may no longer apply, as learners begin to abandon the deficit view of achieving native-like proficiency and embrace the learning resources that come with their first language; they view their linguistic and cultural diversity as a blessing to foster their multilingual ideal self-aspirations rather than a burden for an ideal English self. Another challenge the social-psychological approach confronts is its deterministic view of considering motivation as an individual-difference factor, which does not help to explain the dynamic change of motivation. This is addressed methodologically in Chapter 16 by drawing on conceptualizations from the complex dynamic systems theory. Second, the cognitive-situated approach, which incorporates cognitive theories in language learning motivation in specific learning contexts, can be found in classroom-based studies in this volume. For instance, the self-determination theory (Chapter 7) is outlined as a general theory of motivation and illustrates the dialogic nature of how language learners more involved in decision-making processes can influence their motivation and engagement within their learning environment. Third, the sociodynamic approach, which considers motivation development in a non-linear fashion and within an interplay of multiple personal, social, and contextual factors, is exemplified in Part 3. Taking Chapter 9 for example, the L2 Motivational Self System is elaborated and relevant studies are reviewed to explicate how L2 selves can be explored in motivating language learners in the classroom context.

As for motivation Research Topics, the contributions in this volume have presented a good summary of well-researched motivation topics as well as insightful guidance for future research directions. For example, not only are motivation and engagement explored from multiple perspectives, such as the sociocultural theory (Chapter 2), the goal self-concordance theory (Chapter 6), and the cognitive-behavioral theory (Chapter 13), but also emotions and affect are also incorporated in the probing of language learning motivation in Part 4. As positive psychology takes a firm hold within second language acquisition, researchers have shifted their attention from negative emotional dimensions to positive emotions (Dewaele et al., 2019 ). As motivation researchers begin to equip themselves with new lenses of positive emotions, they can reorientate what they can achieve from creating positive emotions in motivating language learners. Readers can also find inspiration from Part 5, where newly emerging topics are introduced to guide future research. For instance, language learning within the contexts of English as a Lingua Franca (Chapter 19) or plurilingualism (Chapter 18) creates new opportunities in researching language learners' motivation. These new changes bring with them new language learning goals and appraisals of their language proficiency, leading to language learners' different views of looking at language learning obstacles and challenges as well as their own competencies in language learning. Therefore, they can exert a profound impact on language learners' overall motivation, and this less-traveled territory can present a new direction for those who are interested.

While the contributions to this volume can be applauded, there is still room for improvement. For instance, emotional engagement, which is explored in Chapter 13 in Part Four, could be incorporated in Part Two, which addresses topics related to language engagement; Chapter 21 in Part Five could also be included in Part Four, as class group dynamics bears resemblance to group interactions and emotions in the L2 classroom. This being said, it is understandable that the editors, when faced with book chapters that may straddle two sections, make their decisions by weighing the central focus of those chapters.

With these reservations aside, this concise guide, with its international expertise, well-balanced presentation of overviews and emerging topics, as well as its accessible and approachable style, can be a springboard for students and researchers interested in language learning motivation. For postgraduates, it can offer a perspicacious overview of the historical development of the field and provide them with established theories and views; for researchers, the emerging topics and the untrodden territories listed in the afterword by Lourdes Ortega could be enlightening to expand their research within language learning motivation.

Author contributions

RP wrote the initial draft of the article. ZZ provided several rounds of critical and constructive feedback to the drafts, and added comments and analysis. The final draft is the result of RP and ZZ's collective efforts. All authors contributed to the article and approved the submitted version.

Funding Statement

This article was supported by the Hunan Provincial Teaching Reform Project in Higher Learning Institutions (Grant Number: HNJG-2021-0481) and the Scientific Research Project supported by the Department of Education of Hunan Province (Grant Number: 21B0350).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

• Al-Hoorie A. H. (2017). Sixty years of language motivation research: looking back and looking forward . SAGE Open 7 , 1–11. 10.1177/2158244017701976 [ CrossRef ] [ Google Scholar ]
• Al-Hoorie A. H., Szabó F. (eds). (2022). Researching Language Learning Motivation: A Concise Guide. London: Bloomsbury Academic. [ Google Scholar ]
• Dewaele J.-M., Chen X., Padilla A. M., Lake J. (2019). The flowering of positive psychology in foreign language teaching and acquisition research . Front. Psychol. 10 :2128. 10.3389/fpsyg.2019.02128 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dörnyei Z. (2005). The Psychology of the Language Learner: Individual Differences in Second Language Acquisition . Mahwah, NJ: Lawrence Erlbaum. [ Google Scholar ]
• Dörnyei Z., Ryan S. (2015). The Psychology of the Language Learner Revisited . London: Routledge. [ Google Scholar ]
• Gardner R. C. (2010). Motivation and Second Language Acquisition: The Socio-Educational Model . New York, NY: Peter Lang. [ Google Scholar ]
• Ushioda E. (2019). “Researching L2 motivation: past, present and future,” in The Palgrave Handbook of Motivation for Language Learning , eds M. Lamb, K. Csizér, A. Henry, and S. Ryan (Cham: Palgrave Macmillan; Springer; ), 661–682. 10.1007/978-3-030-28380-3_32 [ CrossRef ] [ Google Scholar ]

Princeton University

Princeton engineering, can language models read the genome this one decoded mrna to make better vaccines..

By Scott Lyon

April 8, 2024

Princeton researchers led by Mengdi Wang have developed a language model to home in on partial genome sequences and optimize those sequences to improve function for the development of mRNA vaccines and other therapies. Illustration from Adobe Stock.

The same class of artificial intelligence that made headlines coding software and passing the bar exam has learned to read a different kind of text — the genetic code.

That code contains instructions for all of life’s functions and follows rules not unlike those that govern human languages. Each sequence in a genome adheres to an intricate grammar and syntax, the structures that give rise to meaning. Just as changing a few words can radically alter the impact of a sentence, small variations in a biological sequence can make a huge difference in the forms that sequence encodes.

Now Princeton University researchers led by machine learning expert Mengdi Wang are using language models to home in on partial genome sequences and optimize those sequences to study biology and improve medicine. And they are already underway.

In a paper published April 5 in the journal Nature Machine Intelligence, the authors detail a language model that used its powers of semantic representation to design a more effective mRNA vaccine such as those used to protect against COVID-19.

Found in Translation

Scientists have a simple way to summarize the flow of genetic information. They call it the central dogma of biology. Information moves from DNA to RNA to proteins. Proteins create the structures and functions of living cells.

Messenger RNA, or mRNA, converts the information into proteins in that final step, called translation. But mRNA is interesting. Only part of it holds the code for the protein. The rest is not translated but controls vital aspects of the translation process.

Governing the efficiency of protein production is a key mechanism by which mRNA vaccines work. The researchers focused their language model there, on the untranslated region, to see how they could optimize efficiency and improve vaccines.

After training the model on a small variety of species, the researchers generated hundreds of new optimized sequences and validated those results through lab experiments. The best sequences outperformed several leading benchmarks for vaccine development, including a 33% increase in the overall efficiency of protein production.

Increasing protein production efficiency by even a small amount provides a major boost for emerging therapeutics, according to the researchers. Beyond COVID-19, mRNA vaccines promise to protect against many infectious diseases and cancers.

Wang, a professor of electrical and computer engineering and the principal investigator in this study, said the model’s success also pointed to a more fundamental possibility. Trained on mRNA from a handful of species, it was able to decode nucleotide sequences and reveal something new about gene regulation. Scientists believe gene regulation, one of life’s most basic functions, holds the key to unlocking the origins of disease and disorder. Language models like this one could provide a new way to probe.

Wang’s collaborators include researchers from the biotech firm RVAC Medicines as well as the Stanford University School of Medicine.

The Language of Disease

The new model differs in degree, not kind, from the large language models that power today’s AI chat bots. Instead of being trained on billions of pages of text from the internet, their model was trained on a few hundred thousand sequences. The model also was trained to incorporate additional knowledge about the production of proteins, including structural and energy-related information.

The research team used the trained model to create a library of 211 new sequences. Each was optimized for a desired function, primarily an increase in the efficiency of translation. Those proteins, like the spike protein targeted by COVID-19 vaccines, drive the immune response to infectious disease.

Previous studies have created language models to decode various biological sequences, including proteins and DNA, but this was the first language model to focus on the untranslated region of mRNA. In addition to a boost in overall efficiency, it was also able to predict how well a sequence would perform at a variety of related tasks.

Wang said the real challenge in creating this language model was in understanding the full context of the available data. Training a model requires not only the raw data with all its features but also the downstream consequences of those features. If a program is designed to filter spam from email, each email it trains on would be labeled “spam” or “not spam.” Along the way, the model develops semantic representations that allow it to determine what sequences of words indicate a “spam” label. Therein lies the meaning.

Wang said looking at one narrow dataset and developing a model around it was not enough to be useful for life scientists. She needed to do something new. Because this model was working at the leading edge of biological understanding, the data she found was all over the place.

“Part of my dataset comes from a study where there are measures for efficiency,” Wang said. “Another part of my dataset comes from another study [that] measured expression levels. We also collected unannotated data from multiple resources.” Organizing those parts into one coherent and robust whole — a multifaceted dataset that she could use to train a sophisticated language model — was a massive challenge.

“Training a model is not only about putting together all those sequences, but also putting together sequences with the labels that have been collected so far. This had never been done before.”

The paper, “A 5′ UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions,” was published in Nature Machine Learning. Additional authors include Dan Yu, Yupeng Li, Yue Shen and Jason Zhang, from RVAC Medicines; Le Cong from Stanford; and Yanyi Chu and Kaixuan Huang from Princeton.

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Leveraging the Power of Language Through Assistive Technology

Decades-long research from a team led by Pegasus Professor Jennifer Kent-Walsh has already assisted thousands of children struggling to communicate. An historic grant will now allow them to reach immeasurably more.

By Robert Stephens | April 15, 2024

A five-year $3 million clinical trial grant from the National Institutes of Health (NIH), widely considered the gold standard for biomedical and public health research funding, tells us something unique is again happening at the FAAST Center and Augmentative and Alternative Communication (AAC) Lab at UCF.

“Both the scientific findings and, more importantly, the feedback from the families of children using AAC and their service-providers, have shown us the incredible power assistive technology can hold for children with significant speech impairments when combined with customized language therapy,” says Jennifer Kent-Walsh, founder of UCF’s Assistive Technology Center and a Pegasus Professor in the College of Health Professions and Sciences . “This new round of NIH funding means we can expand our work to provide clinicians with more evidence-based assessment and intervention options to help children develop language skills through use of AAC technologies.”

The NIH can clearly see from data that children with significant speech impairments and genetic conditions like Down syndrome and cerebral palsy are communicating at higher levels. They can also look at the flurry of activity and engagement among the children, families, service providers, UCF students, community clinicians, caregivers and researchers at the center. Thousands of people have come for workshops, individualized training and therapy, and to borrow assistive technologies to help those struggling with language development, communication and a wide range of other needs.

“We are fortunate to have had support at UCF to align the critical stakeholder groups required to simultaneously advance science and practice — community, clinical, academic, research, industry, and the next generation of speech-language pathologists and related service-providers,” Kent-Walsh says. “Two key aspects of the study this new funding will support relate to the critical need to design interventions that are implementable in real-life contexts for clinicians and accessible to as many children as possible for sustained periods of time in community settings.”

Kent-Walsh argues that engineering great technologies and even designing effective interventions can still miss the mark of meaningful scientific advancement in healthcare without implementation. This new funding will allow Kent-Walsh’s team to focus on validating interventions that are designed with and for clinicians supporting children using assistive technology to communicate.  The fact that the NIH is funding this work is a sign of the significant and practical progress that’s been two decades in the making.

Kent-Walsh is the cornerstone of this classic from-the-ground-up story. She came to UCF as an assistant professor of communication sciences and disorders in 2003. As a teacher and speech-language pathologist in Canada and England, she had witnessed first-hand the life setbacks that speech and language disorders can cause for children. At UCF, she would have the freedom to explore meaningful solutions, including technology-based ideas, to address these challenges. “The university always had an openness for innovation – particularly in areas relating to engineering and technology,” she says. “For me, it was exciting to have the opportunity to develop a program of assistive technology research in an environment already primed for technology development and with an emerging focus on health and wellness.”

“Communication is a basic human right which can, and must, be supported for all.”

During her time at UCF, Kent-Walsh has secured millions of dollars in funding from local, state and federal sources to expand research and optimize assistive technology service-delivery. In 2023, after being named a Pegasus Professor, Kent-Walsh saw a banner hung in her honor with the words that have driven her from day one:

“Communication is a basic human right which can, and must, be supported for all,” Kent-Walsh says.

When any of this is brought up — the research, the scientific findings and the successes — Kent-Walsh shifts the focus to the power of people working together.

“We have been able to learn and accomplish as much as we have to this point through true team science,” she says.

Cathy Binger at the University of New Mexico (UNM) has been Kent-Walsh’s primary research collaborator for the past two decades and she serves as the other principal investigator for this new grant. Their decades long partnership has afforded invaluable cross-institutional learning and funded training experiences for both UCF and UNM students through clinical trial investigations like this one. Professor John Heilman, from the University of Wisconsin-Milwaukee, recently joined the research team as a language measurement expert. Other key team members include Professor Debbie Hahs-Vaughn from UCF’s College of Community Innovation and Education, who serves as biostatistician for the project, and associate clinical instructor Nancy Harrington who serves as project director for this multi-site clinical trial investigation.  And then there is the project team — with additional clinical and academic connections for the project facilitated by the broader village of collaborators in the FAAST Center and AAC Lab, including associate clinical instructor Carolyn Buchanan and clinical instructor Punam Desormes.

“When we involve students and our community at large, the network of advocates expands to ensure that any child can gain access to assistive technology services,” Kent-Walsh says. “They know, and NIH knows, that there is a growing body of findings indicating that the use of AAC technologies, combined with tailored language intervention, is where the magic happens.”

The team’s overall goal is to reach as many children as possible in as many healthcare settings as possible, and ultimately, to improve lives.

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AI Index Report

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine.

Read the 2024 AI Index Report

The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year’s edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

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A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.

Progress accelerated in 2023. New state-of-the-art systems like GPT-4, Gemini, and Claude 3 are impressively multimodal: They can generate fluent text in dozens of languages, process audio, and even explain memes. As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.

AI faces two interrelated futures. First, technology continues to improve and is increasingly used, having major consequences for productivity and employment. It can be put to both good and bad uses. In the second future, the adoption of AI is constrained by the limitations of the technology. Regardless of which future unfolds, governments are increasingly concerned. They are stepping in to encourage the upside, such as funding university R&D and incentivizing private investment. Governments are also aiming to manage the potential downsides, such as impacts on employment, privacy concerns, misinformation, and intellectual property rights.

As AI rapidly evolves, the AI Index aims to help the AI community, policymakers, business leaders, journalists, and the general public navigate this complex landscape. It provides ongoing, objective snapshots tracking several key areas: technical progress in AI capabilities, the community and investments driving AI development and deployment, public opinion on current and potential future impacts, and policy measures taken to stimulate AI innovation while managing its risks and challenges. By comprehensively monitoring the AI ecosystem, the Index serves as an important resource for understanding this transformative technological force.

On the technical front, this year’s AI Index reports that the number of new large language models released worldwide in 2023 doubled over the previous year. Two-thirds were open-source, but the highest-performing models came from industry players with closed systems. Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark; performance on the benchmark has improved by 15 percentage points since last year. Additionally, GPT-4 achieved an impressive 0.97 mean win rate score on the comprehensive Holistic Evaluation of Language Models (HELM) benchmark, which includes MMLU among other evaluations.

Although global private investment in AI decreased for the second consecutive year, investment in generative AI skyrocketed. More Fortune 500 earnings calls mentioned AI than ever before, and new studies show that AI tangibly boosts worker productivity. On the policymaking front, global mentions of AI in legislative proceedings have never been higher. U.S. regulators passed more AI-related regulations in 2023 than ever before. Still, many expressed concerns about AI’s ability to generate deepfakes and impact elections. The public became more aware of AI, and studies suggest that they responded with nervousness.

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3 Questions: Enhancing last-mile logistics with machine learning

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Across the country, hundreds of thousands of drivers deliver packages and parcels to customers and companies each day, with many click-to-door times averaging only a few days. Coordinating a supply chain feat of this magnitude in a predictable and timely way is a longstanding problem of operations research, where researchers have been working to optimize the last leg of delivery routes. This is because the last phase of the process is often the costliest due to inefficiencies like long distances between stops due to increased ecommerce demand, weather delays, traffic, lack of parking availability, customer delivery preferences, or partially full trucks — inefficiencies that became more exaggerated and evident during the pandemic.

With newer technology and more individualized and nuanced data, researchers are able to develop models with better routing options but at the same time need to balance the computational cost of running them. Matthias Winkenbach, MIT principal research scientist, director of research for the MIT Center for Transportation and Logistics (CTL) and a researcher with the MIT-IBM Watson AI Lab, discusses how artificial intelligence could provide better and more computationally efficient solutions to a combinatorial optimization problem like this one.

Q: What is the vehicle routing problem, and how do traditional operations research (OR) methods address it?

A: The vehicle routing problem is faced by pretty much every logistics and delivery company like USPS, Amazon, UPS, FedEx, DHL every single day. Simply speaking, it's finding an efficient route that connects a set of customers that need to be either delivered to, or something needs to be picked up from them. It’s deciding which customers each of those vehicles — that you see out there on the road — should visit on a given day and in which sequence. Usually, the objective there is to find routes that lead to the shortest, or the fastest, or the cheapest route. But very often they are also driven by constraints that are specific to a customer. For instance, if you have a customer who has a delivery time window specified, or a customer on the 15th floor in the high-rise building versus the ground floor. This makes these customers more difficult to integrate into an efficient delivery route.

To solve the vehicle routing problem, we obviously we can't do our modeling without proper demand information and, ideally, customer-related characteristics. For instance, we need to know the size or weight of the packages ordered by a given customer, or how many units of a certain product need to be shipped to a certain location. All of this determines the time that you would need to service that particular stop. For realistic problems, you also want to know where the driver can park the vehicle safely. Traditionally, a route planner had to come up with good estimates for these parameters, so very often you find models and planning tools that are making blanket assumptions because there weren’t stop-specific data available.

Machine learning can be very interesting for this because nowadays most of the drivers have smartphones or GPS trackers, so there is a ton of information as to how long it takes to deliver a package. You can now, at scale, in a somewhat automated way, extract that information and calibrate every single stop to be modeled in a realistic way.

Using a traditional OR approach means you write up an optimization model, where you start by defining the objective function. In most cases that's some sort of cost function. Then there are a bunch of other equations that define the inner workings of a routing problem. For instance, you must tell the model that, if the vehicle visits a customer, it also needs to leave the customer again. In academic terms, that's usually called flow conservation. Similarly, you need to make sure that every customer is visited exactly once on a given route. These and many other real-world constraints together define what constitutes a viable route. It may seem obvious to us, but this needs to be encoded explicitly.

Once an optimization problem is formulated, there are algorithms out there that help us find the best possible solution; we refer to them as solvers. Over time they find solutions that comply with all the constraints. Then, it tries to find routes that are better and better, so cheaper and cheaper ones until you either say, "OK, this is good enough for me," or until it can mathematically prove that it found the optimal solution. The average delivery vehicle in a U.S. city makes about 120 stops. It can take a while to solve that explicitly, so that's usually not what companies do, because it's just too computationally expensive. Therefore, they use so-called heuristics, which are algorithms that are very efficient in finding reasonably good solutions but typically cannot quantify how far away these solutions are from the theoretical optimum.

Q: You’re currently applying machine learning to the vehicle routing problem. How are you employing it to leverage and possibly outperform traditional OR methods?

A: That's what we're currently working on with folks from the MIT-IBM Watson AI Lab. Here, the general idea is that you train a model on a large set of existing routing solutions that you either observed in a company’s real-world operations or that you generated using one of these efficient heuristics. In most machine-learning models, you no longer have an explicit objective function. Instead, you need to make the model understand what kind of problem it's actually looking at and what a good solution to the problem looks like. For instance, similar to training a large language model on words in a given language, you need to train a route learning model on the concept of the various delivery stops and their demand characteristics. Like understanding the inherent grammar of natural language, your model needs to understand how to connect these delivery stops in a way that results in a good solution — in our case, a cheap or fast solution. If you then throw a completely new set of customer demands at it, it will still be able to connect the dots quite literally in a way that you would also do if you were trying to find a good route to connect these customers.

For this, we're using model architectures that most people know from the language processing space. It seems a little bit counterintuitive because what does language processing have to do with routing? But actually, the properties of these models, especially transformer models, are good at finding structure in language — connecting words in a way that they form sentences. For instance, in a language, you have a certain vocabulary, and that's fixed. It's a discrete set of possible words that you can use, and the challenge is to combine them in a meaningful way. In routing, it's similar. In Cambridge there are like 40,000 addresses that you can visit. Usually, it's a subset of these addresses that need to be visited, and the challenge is: How do we combine this subset — these "words" — in a sequence that makes sense?

That's kind of the novelty of our approach — leveraging that structure that has proven to be extremely effective in the language space and bringing it into combinatorial optimization. Routing is just a great test bed for us because it's the most fundamental problem in the logistics industry. 

Of course, there are already very good routing algorithms out there that emerged from decades of operations research. What we are trying to do in this project is show that with a completely different, purely machine learning-based methodological approach, we are able to predict routes that are pretty much as good as, or better than, the routes that you would get from running a state-of-the-art route optimization heuristic.

Q: What advantages does a method like yours have over other state-of-the-art OR techniques?

A: Right now, the best methods are still very hungry in terms of computational resources that are required to train these models, but you can front-load some of this effort. Then, the trained model is relatively efficient in producing a new solution as it becomes required. 

Another aspect to consider is that the operational environment of a route, especially in cities, is constantly changing. The available road infrastructure, or traffic rules and speed limits might be altered, the ideal parking lot may be occupied by something else, or a construction site might block a road. With a pure OR-based approach, you might actually be in trouble because you would have to basically resolve the entire problem instantly once new information about the problem becomes available. Since the operational environment is dynamically changing, you would have to do this over and over again. While if you have a well-trained model that has seen similar issues before, it could potentially suggest the next-best route to take, almost instantaneously. It's more of a tool that would help companies to adjust to increasingly unpredictable changes in the environment.

Moreover, optimization algorithms are often manually crafted to solve the specific problem of a given company. The quality of the solutions obtained from such explicit algorithms is bounded by the level of detail and sophistication that went into the design of the algorithm. A learning-based model, on the other hand, continuously learns a routing policy from data. Once you have defined the model structure, a well-designed route learning model will distill potential improvements to your routing policy from the vast amount of routes it is being trained on. Simply put, a learning-based routing tool will continue to find improvements to your routes without you having to invest into explicitly designing these improvements into the algorithm.

Lastly, optimization-based methods are typically limited to optimizing for a very clearly defined objective function, which often seeks to minimize cost or maximize profits. In reality, the objectives that companies and drivers face are much more complex than that, and often they are also somewhat contradictory. For instance, a company wants to find efficient routes, but it also wants to have a low emissions footprint. The driver also wants to be safe and have a convenient way of serving these customers. On top of all of that, companies also care about consistency. A well-designed route learning model can eventually capture these high-dimensional objectives by itself, and that is something that you would never be able to achieve in the same way with a traditional optimization approach.

So, this is the kind of machine learning application that can actually have a tangible real-world impact in industry, on society, and on the environment. The logistics industry has problems that are much more complex than this. For instance, if you want to optimize an entire supply chain — let's say, the flow of a product from the manufacturer in China through the network of different ports around the world, through the distribution network of a big retailer in North America to your store where you actually buy it — there are so many decisions involved in that, which obviously makes it a much harder task than optimizing a single vehicle route. Our hope is that with this initial work, we can lay the foundation for research and also private sector development efforts to build tools that will eventually enable better end-to-end supply chain optimization.

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• Matthias Winkenbach
• MIT-IBM Watson AI Lab
• Center for Transportation and Logistics

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• MIT Sloan School of Management
• Artificial intelligence
• Supply chains
• Transportation
• Business and management

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Undergraduate Research Assistant Opportunities at (MURGE Lab) on Large Language Model Reasoning/Uncertainty/Multi-agent Interactions/Multimodality

Advisor: Prof. Mohit Bansal (https://www.cs.unc.edu/~mbansal/) Mentor: Elias Stengel-Eskin (https://esteng.github.io) Group: Murge Lab UNC-CH (https://murgelab.cs.unc.edu/) Duration (Flexible): Apr 15th – Sep 15th, 2024 (5 months), at least 20 hours per week commitment. Role: Research Assistant (with RAship stipend) Contact: Elias Stengel-Eskin ([email protected]) with some basic information about yourself, your transcripts, your CV, and any discussion about any prior Machine Learning / Programming experience you have.

Requirements from Candidates (Good to Haves): – Undergrads or Masters students from Computer Science, Mathematics/Statistics, or Linguistics/Cognitive Science background. – Strong foundation in machine learning and deep learning techniques. Familiarity with model architectures like transformers, etc. – Familiarity with deep learning programming frameworks like Pytorch and Huggingface libraries. – Preferred: experience with code generation (semantic parsing, text-to-code) or vision-language tasks. – Strong analytical abilities to ask the right questions to come up with a hypothesis and then design experiments to test it. – Candidates who are possibly interested in a research career or grad school (Master/PhD) or Machine Learning jobs in the future.

Project Description:

Large language models allow people to interact with digital systems using natural language. However, language is inherently ambiguous and underspecified, and can lead to high uncertainty. This project will focus on enabling safe and robust interactions that allow people to make informed choices on when to trust model outputs. Core topics include: how to better model uncertainty, how to leverage multiple models to address ambiguity, how to intelligently obtain information to reduce uncertainty, and how to produce outputs that help users form their own uncertainty estimates. Topics may also include: multimodality and multimodal generation, vision-language tasks.

Research Areas: – Multi-agent interactions and collaboration. – Acquiring information for uncertainty reduction. – Calibration and confidence estimation in LLM reasoning. – Attributable and interpretable text generation. – Multimodal reasoning and generation.

Some recent and representative papers in the direction: 1. Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models, ICLR 2024 (https://arxiv.org/abs/2310.05861) 2. ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs (https://arxiv.org/pdf/2309.13007) 3. MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models (https://arxiv.org/abs/2402.01620) 4. Soft Self-Consistency Improves Language Model Agents (https://arxiv.org/abs/2402.13212) 5. Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training (https://arxiv.org/abs/2403.02325) 6. Did You Mean…? Confidence-based Trade-offs in Semantic Parsing, EMNLP 2023 (https://arxiv.org/abs/2303.16857) 7. Zero and Few-shot Semantic Parsing with Ambiguous Inputs, ICLR 2024 (https://arxiv.org/abs/2306.00824) 8. ReGAL: Refactoring Programs to Discover Generalizable Abstractions (https://arxiv.org/abs/2401.16467) 9. GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations (https://arxiv.org/abs/2402.12348) 10. VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning (https://arxiv.org/abs/2309.15091) 11. Any-to-Any Generation via Composable Diffusion (https://arxiv.org/abs/2305.11846)

For inquiries or to express your interest, please send an email to [email protected]