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Unlike other public health crises, attention to the role of prevention in racial bias has not predominated. Most human actions, including racism, are informed by unconscious thoughts. Behavior-change interventions seek to understand facilitators and barriers to human action and antecedent unconscious thoughts, which are guided not only within an individual but also in interpersonal and societal environments. Current behavioral interventions on implicit and explicit racial bias can identify gaps and opportunities in the literature to evaluate operational definitions of behavior and bias, discuss psychological and neurobiological processes involved in racial bias, that may provide insight into prevention. Furthermore, a focus on public health–based interventions which integrate behavioral science foundations may assist to develop adaptable, accurate, and effective interventions across global communities. Based on the literature results discussed, the benefit for the field of public health may be to inform future studies and create a multilevel, behavioral-based framework to prevent or mitigate racial bias behaviors..

In the public and private sectors, women continue to address multiple hurdles despite diversity and equity initiatives. Women have made tremendous strides in the workforce but are still a minority in leadership positions worldwide in multiple sectors, including nonprofit, corporate, government, medicine, education, military, and religion. In the United States women represent 60% of bachelor’s degrees earned at universities and outpace men in master’s and doctoral programs. However, a significant body of research illustrates that women’s upward mobility has been concentrated in middle management positions. Women hold 52% of all management and professional roles in the U.S. job market, including physicians and attorneys. Yet women fall behind in representation in senior level positions. In the legal profession, for example, women represent 45% of associates but only 22.7% are partners. In medicine, women represent 40% of all physicians and surgeons but only 16% are permanent medical school deans. In academia, women surpass men in doctorates but only 32% are full professors. Furthermore, only 5% of chief executive officers (CEOs) in Fortune 500 companies and 19% of the board members in companies included in Standard & Poor’s (S&P) Composite 1500 Index are women. Progress is even more elusive for women of color despite making up 38.3% of the female civilian labor force. Only two women of color are Fortune 500 CEOs and only 4.7% of women are executive or senior level official managers in S&P 1500 companies. There are more women in leadership positions in the public sector than in the private sector. In 2014, 43.5% of women between the ages of 23 and 34 were managers at public companies, compared to 26% in similar positions in the private sector. In 2018, 127 women were elected to the U.S. Congress and 47 of those serving in 2018 were women of color. In addition, the first Native American woman, first Muslim woman, and Congress’s youngest woman were elected in that year. However, there is still progress to be made to close the gap, especially in senior-level positions. The significance of these statistics is staggering and confirms the need for attention. The percentage of women holding leadership positions in the public and private sectors, especially in business and education, has grown steadily in the past decade. However, subtle barriers like bias and stereotypes unfavorably encumber women’s career progression and are often used to explain the lack of women in leadership positions.

The concept of aversive racism has had a significant impact on theory, research, and practice devoted to better understanding bias, discrimination, and persistent disparities based on social identity group such as race, gender, social class, and so on. Originally developed to better explain subtle forms of bias toward racial and minoritized groups, this concept has been extended to understand the impact of disparities in a range of diverse settings, such as intergroup relations, health outcomes, fairness in employment setting, intergroup conflict, educational outcomes, racial bias in policing, experiences of stress and mental health issues, and persistent economic disparities. A core facet of the aversive framework paradigm is that because of human biases that are deeply rooted within a historical context and reinforced by ongoing societal ideologies, unintentional and subtle forms of discrimination emerge and persist. Given that these subtle forms of bias and discrimination exist within otherwise well-intentioned individuals, strategies to eliminate them require understanding the complexity of the aversive racism phenomenon in order to develop effective social interventions. This article reviews the foundation, research, and impact of this important body of work. In addition, the concept of aversive racism is discussed in connection to emerging research on microaggressions and unconscious (implicit) bias in order to create a more integrated framework that can shape future research and applications. Lastly, practical implications for organizations and future directions are explored, such as using social identity as a theoretical lens, including global perspectives on intergroup bias and leveraging emerging work on intersectionality, as useful perspectives to extend the aversive racism framework. Setting a future agenda for research and practice related to aversive racism is key to greater understanding of how to reduce intergroup bias and discrimination through interventions that cut across traditional academic and discipline boundaries as one approach to create meaningful and long-lasting social impact.

Stereotypes are a central concern in society and in the workplace. Stereotypes are cognitions that drive what individuals know, believe, and expect from others as a result of their social identities. Stereotypes predict how individuals view and treat one another at work, often resulting in inaccurate generalizations about individuals based on their group membership. As such, it’s important to break down and combat the use of stereotypes in decision-making at work. If stereotypes can be overcome in the workplace, fairness and equity in organizations becomes more likely.

Bias and equivalence provide a framework for methodological aspects of cross-cultural studies. Bias is a generic term for any systematic errors in the measurement that endanger the comparability of cross-cultural data; bias results in invalid comparative conclusions. The demonstration of equivalence (i.e., absence of bias) is a prerequisite for any cross-cultural comparison. Based on the source of incomparability, three types of bias, namely construct, method, and item bias, can be distinguished. Correspondingly, three levels of equivalence, namely, construct, metric, and scalar equivalence, can be distinguished. One of the goals in cross-cultural research is to minimize bias and enhance comparability. The definitions and manifestations of these types of bias and equivalence are described and remedies to minimize bias and enhance equivalence at the design, implementation, and statistical analysis phases of a cross-cultural study are provided. These strategies involve different research features (e.g., decentering and convergence), extensive pilot and pretesting, and various statistical procedures to demonstration of different levels of equivalence and detections of bias (e.g., factor analysis based approaches and differential item functioning analysis). The implications of bias and equivalence also extend to instrument adaptation and combining etic and emic approaches to maximize the ecological validity. Instrument choices in cross-cultural research and the categorization of adaptations stemming from considerations of the concept, culture, language, and measurement are outlined. Examples from cross-cultural research of personality are highlighted to illustrate the importance of combining etic and emic approaches. The professionalization and broadening of the field is expected to increase the validity of conclusions regarding cross-cultural similarities and differences.

Corporations often start their diversity journey by providing their managers or all workers with diversity training. These trainings were first offered as race-relations sessions in the 1960s and are among the most popular tools for diversity managers. Diversity training programs have changed their content during the decades, but they usually include live or online explanations about unlawful discrimination and bias, often supplemented by discussions of cultural differences and business needs for diversity. Despite their popularity and often high costs, a large body of research conducted over decades shows that most diversity training programs do not lead to long-term improvements in participants’ bias, attitudes, behavior, or workforce diversity. Some studies also show that training has negative effects on bias and diversity. Factors that impede the success of diversity training or make them backfire include the hardwiring of cognitive biases and people’s complex reactions to direct attempts to change their biases, as well as the broader systemic biases rooted in everyday organizational routines. These suggest that common diversity training simply may not be the right tool for reducing bias and generating the changes needed for increasing workplace diversity. Some studies suggest that trainings’ effects could be improved by carefully designing them. This includes: avoiding training features that increase participants’ alienation, such as mandatory attendance, quizzes, and legalistic content; designing long-term training such that meaningful learning can be achieved; calibrating training to specific organizational challenges rather than using off-the-shelf content; and including ongoing collaborative contact with members of underrepresented groups and integrating training as part of broader diversity and accountability efforts. More research is needed to determine whether these types of training indeed produce sustained improvements in bias and diversity. Alternative bias reduction mechanisms can be found in popular management models that increase collaboration between workers, such as cross-functional teams and cross-training. Such collaborative teams and training improve corporate performance and, as a byproduct, also reduce bias. Cognitive biases are affected by the work contexts in which individuals operate. Highly segregated workplaces, where White people and men meet women and people of color (or other underrepresented groups) primarily in marginalized jobs, deepen group boundaries and strengthen stereotypes. When organizations create cross-functional collaborations using self-directed teams and cross-training, workers from different groups have more opportunities to collaborate and, as studies show, biases and group boundaries are reduced, and leadership diversity increases.

Who filters through information and determines what information is shared with media audiences? Who filters through information and determines what information will not be shared with media audiences? Ultimately, who controls the flow of information in the media? At times commentary pertaining to media content references media as an omnipotent individual entity selecting the content transmitted to the public, reminiscent of a Wizard of Oz manner of the all-powerful being behind the curtain. Overlooked in this perception is the reality that in mass media, there are various individuals in positions of power making decisions about the information accessed by audiences of various forms of media. These individuals are considered gatekeepers: wherein the media functions as a gate permitting some matters to be publicized and included into the public discourse while restricting other matters from making it to the public conscience. Media gatekeepers (i.e., journalists, editors) possess the power to control the gate by determining the content delivered to audiences, opening and closing the gate of information. Gatekeepers wield power over those on the other side of the gate, those seeking to be informed (audiences), as well as those seeking to inform (politics, activists, academics, etc.). The earliest intellectual explanation of gatekeeping is traced to Kurt Lewin, describing gatekeeping as a means to analyze real-world problems and observing the effects of cultural values and subjective attitudes on those problems like the distribution of food in Lewins’s seminal study, and later modified by David Manning White to examine the dissemination of information via media. In an ideal situation, the gatekeepers would be taking on the challenge of weighing the evidence of importance in social problems when selecting among the options of content and information to exhibit. Yet, decisions concerning content selection are not void of subjective viewpoints and encompass values, beliefs, and ideals of gatekeepers. The subjective attitudes of gatekeepers influence their perspective of what qualifies as newsworthy information. Hence, those in the position to determine the content transmitted through media exercise the power to shape social reality for media audiences. In the evolution of media gatekeeping theory three models have resulted from the scholarship: (1) examination of the one-way flow of information passing through a series of gates before reaching audiences, (2) the process of newsroom personnel interacting with people outside of the newsroom, and (3) the direct communication of private citizens and public officials. In traditional media and newer forms of social media, gatekeeping examination revolves around analysis of these media organizations’ news routines and narratives. Gatekeeping analysis observes human behavior and motives in order to make conceptualizations about the social world.

Despite the term being coined in the early 1990s, heteronormativity is a longstanding and enduring hierarchical social system that identifies heterosexuality as the standard sexuality and normalizes gender-specific behaviors and roles for men, women, and transgender and non-binary individuals. As a system, it defines and enforces beliefs and practices about what is ‘normal’ in everyday life. Although there are many factors that shape heteronormative beliefs and attitudes, religion, the government, education, and workplaces are the principal macro-level factors that normalize and institutionalize heteronormative beliefs and attitudes. These institutions contribute an outsize influence on the perpetuation of heteronormativity in society because these institutions create and inculcate the norms and standards of what are and are not acceptable values, attitudes, beliefs, and behaviors in our society. As such, in order to create effective interventions to eliminate the negative outcomes of heteronormativity, particular attention should be paid to each of these institutions. Parents, relatives, and other adults contribute to the normalization and institutionalization of heteronormativity at the individual- or micro-level. Although some people benefit from the system of heteronormativity (mainly heterosexual cisgender conforming men), much of the research on heteronormativity focuses on the negative outcomes. Heteronormativity is responsible for a host of pernicious outcomes such as lower self-esteem, job satisfaction, and organizational commitment, and greater rates of suicide ideation, verbal and physical abuse, and workplace mistreatment and discrimination. Future research should investigate identify effective micro- and macro-level interventions that could mitigate or eliminate the negative effects of heteronormativity.

Behavior is a reflection of the intentions, attitudes, goals, beliefs, and desires of a person. These intra-individual factors are coordinated with what opportunities the situation affords and the perceived constraints placed on the person by their context and the norms of the culture they are in. Further, the intentions, attitudes, goals, beliefs, and desires of a person are often not known to them in any given moment, and because they reside within the mind of that person they are almost always not known to the people who are perceiving that person. To know anything about other people we must observe and identify/classify their behavior and then attribute to the observed behavior inferences and judgments about the internal states of that person serving as the motivating force behind their behavior. This entry explores this process of attribution. Heider described attribution as the process that determines “how one person thinks and feels about another person, how he perceives him and what he does to him, what he expects him to do or think, how he reacts to the actions of the other.” The entry explores the rules that people follow in order to make sense of behavior, and the rational versus non-rational nature of the procedure. Even when highly motivated to think rationally, this process can be biased, and flaws can appear in the attribution process, such as from chronic differences among perceivers due to culture, experience, or personality. How the process would unfold if accurate and purely rational is contrasted with how it unfolds when biased. How we feel, and how we choose to act, are derived from how we make sense of the world. Thus, attribution processes are foundational for understanding how we feel, for establishing expectations, and planning how to act in turn.

Leaders exist in myriad types of groups. Yet in many of them—including in organizational, political, and educational domains—leadership roles are disproportionately occupied by individuals of certain social categories (e.g., men, white individuals). Speaking to this imbalance in representation, there is a wealth of theory and research indicating that gender and race are key to understanding: (a) who tends to get placed in leadership roles, and (b) what an individual’s experience will be like while in that role or on the path to it. In part, this is because there are commonly held stereotypes that make certain individuals—often those of socially dominant racial and gender groups—seem better suited for leadership. By comparison, individuals of other genders and races are often perceived and evaluated as less suitable and treated as such (e.g., deprived of opportunities to become leaders or develop leadership skills). These stereotypes can also elicit disparate internal states (e.g., stereotype threat, internalized negative self-perceptions) that affect individuals’ likelihood of pursuing or obtaining such roles (e.g., by affecting their motivation or performance). In this way, leadership dynamics are intimately connected to the study of gender and race. Overall, these dynamics involve several psychological processes. This includes myriad forms of gender and racial bias—discrimination in evaluations, pay, hiring, promotions, and in access to role models, mentorship, and support; backlash effects, queen bee effects (self-group distancing), glass cliff effects, motherhood penalties, and fatherhood bonuses. It also involves multiple lines of theorizing—role congruity theory, lack of fit, masculine defaults and ambient belonging, modern sexism, aversive racism, social identity threat, and others. Looking ahead, there are several critical directions for advancing research on gender, race, and leadership. This includes examining leadership processes from a more precise, intersectional lens rather than studying the implications of one’s gender or race in isolation (e.g., by integrating work on intersectionality theory, gendered races, and intersectional invisibility). Future study of these processes will also need to consider other relevant social identities (e.g., reflecting class, religion, age, sexuality, ability and neurodiversity, nationality, and immigration status), along with a more thorough consideration of gender—going beyond the study of (cisgender) men and women to consider how transgender and gender-nonconforming individuals are perceived and treated in leadership roles or on the path to such roles. Additionally, and ultimately, it will be critical to develop effective strategies for addressing the underrepresentation of women, racial and ethnic minorities, and other social groups in leadership. In part this will mean carefully evaluating strategies now being employed (e.g., organizational diversity messages, quotas and affirmative action, mentorship programs)—including those that may be largely ineffective, if not causing harm (e.g., implicit bias training, campaigning for women to “lean in”). Addressing the lack of diversity in leadership will be a crucial step toward tackling broader issues of social inequity.

Much of our sport and physical activity behavior is regulated by processes occurring outside of conscious awareness. In contrast, most sport and physical activity research focuses on processes that are easily accessible by conscious introspection. More and more, however, research is demonstrating that automatic regulation is instrumental to our understanding of how to get people to maintain a physically active lifestyle and how to get the most out of people’s sports performance potential. Automatic regulation is the influence on our thoughts and actions that result from the mental network of associations we use to make sense of the world around us. Habits are automatic associations of cues with behavioral responses. Automatic evaluations are automatic associations of cues as being good or bad. Automatic schemas are automatic associations of cues with actual or ideal self-identity. These processes have been assessed with implicit measures by making indirect inferences from self-report or response latency tasks. Emerging research demonstrates that automatic associations influence sport performance and physical activity behavior, but further work is still needed to establish which type of automatic regulation is responsible for these influences and how automatic regulation and reflective processes interact to impact movement.

Home bias in international macroeconomics refers to the fact that investors around the world tend to allocate majority of their portfolios into domestic assets, despite the potential benefits to be had from international diversification. This phenomenon has been occurring across countries, over time, and across equity or bond portfolios. The bias towards domestic assets tends to be larger in developing countries relative to developed economies, with Europe characterized by the lowest equity home bias, while Central and South America—by the highest equity home bias. In addition, despite the secular decline in the level of equity home bias over time in all countries and regions, home bias still remains a robust feature of the data. Whether home bias is a puzzle depends on the portfolio allocation that one uses as a theoretical benchmark. For instance, home bias in equity portfolio is a puzzle when assessed through the lens of a simple international capital asset pricing model (CAPM) with homogeneous investors. This model predicts that investors should hold world market portfolios, namely a portfolio with the share of domestic asset equal to the share of those assets in the world market portfolio. For instance, since the share of US equity in the world capitalization in 2016 was 56%, then US investors should allocate 56% of their equity portfolio into local assets, while investing the remaining 44% into foreign equities. Instead, foreign equity comprised just 23% of US equity portfolio in 2016, hence the equity home bias. Alternative portfolio benchmark comes from the theories that emphasize costs for trading assets in international financial markets. These include transaction and information costs, differential tax treatments, and more broadly, differences in institutional environments. This research, however, has so far been unable to reach a consensus on the explanatory power of such costs. Yet another theory argues that equity home bias can arise due to the hedging properties of local equity. In particular, local equity can provide insurance from real exchange rate risk and non-tradable income risk (such as labor income risk), and thus a preference towards home equities is not a puzzle, but rather an optimal response to such risks. These theories, main advances and results in the macroeconomic literature on home bias are discussed in this article. It starts by presenting some empirical facts on the extent and dynamics of equity home bias in developed and developing countries. It is then shown how home bias can arise as an equilibrium outcome of the hedging demand in the model with real exchange rate and non-tradable labor income risk. Since solving models with portfolio choice is challenging, the recent advances in solving such models are also outlined in this article. Integrating the portfolio dynamics into models that can generate realistic asset price and exchange rate dynamics remains a fruitful avenue for future research. A discussion of additional open questions in this research agenda and suggestions for further readings are also provided.

Researchers are more likely to share notable findings. As a result, published findings tend to overstate the magnitude of real-world phenomena. This bias is a natural concern for asset pricing research, which has found hundreds of return predictors and little consensus on their origins. Empirical evidence on publication bias comes from large-scale metastudies. Metastudies of cross-sectional return predictability have settled on four stylized facts that demonstrate publication bias is not a dominant factor: (a) almost all findings can be replicated, (b) predictability persists out-of-sample, (c) empirical t-statistics are much larger than 2.0, and (d) predictors are weakly correlated. Each of these facts has been demonstrated in at least three metastudies. Empirical Bayes statistics turn these facts into publication bias corrections. Estimates from three metastudies find that the average correction (shrinkage) accounts for only 10%–15% of in-sample mean returns and that the risk of inference going in the wrong direction (the false discovery rate) is less than 10%. Metastudies also find that t-statistic hurdles exceed 3.0 in multiple testing algorithms and that returns are 30%–50% weaker in alternative portfolio tests. These facts are easily misinterpreted as evidence of publication bias. Other misinterpretations include the conflating of phrases such as “mostly false findings” with “many insignificant findings,” “data snooping” with “liquidity effects,” and “failed replications” with “insignificant ad-hoc trading strategies.” Cross-sectional predictability may not be representative of other fields. Metastudies of real-time equity premium prediction imply a much larger effect of publication bias, although the evidence is not nearly as abundant as it is in the cross section. Measuring publication bias in areas other than cross-sectional predictability remains an important area for future research.

Hate crimes and their traumatic repercussions are an important area for social worker intervention. This entry will examine how hate crimes are defined and handled, and the difficulties inherent in categorizing and responding to them. Collection of hate crime statistics and hate crime–related legislation are reviewed. The entry will also examine how social workers can help victims and perpetrators and influence how society conceptualizes and prevents hate crimes and their consequences.

Motivated reasoning is a pervasive force in politics. The concept of motivated reasoning was developed and elaborated in both psychology and economics as a way of understanding the way in which people learn and respond to information, and as a mechanism to explain behavior that is seen as less than optimal. It has been important in political science since the early 2000s. Political scientists initially connected the distinct constructs of motivated reasoning with online information processing, given the affective, unconscious, and automatic bases of both processes, but they later distinguished cognitive effort and processing style from underlying goals or motivation. Most research on motivated reasoning in political science focuses on two primary motivations: accuracy and directional goals. An accuracy motivation is defined by reasoning that seeks to arrive at a conclusion that is free from error given the information at hand, whereas a directional goal is defined by a desire to protect one’s existing beliefs or identities. Much of the early research on motivated reasoning in political science painted citizens as incapable of evaluating information objectively, and, to make matters worse, citizens’ processing biases appeared to increase with political knowledge, interest, and the strength of existing political beliefs. A second generation of research on motivated reasoning identified individual-level and contextual factors that moderated or eliminated directional biases. Scholars developed a better understanding of how motivations rooted in partisan identity affect information interpretation, evaluation, and decision-making, as well how different information environments can shift the motivations that citizens pursue when they are reasoning about politics. The pursuit of an accuracy motivation in political reasoning is now considered a realistic and attainable standard for evaluating citizen competence in democratic societies that avoids many of the pitfalls of past attempts to define the quality of citizens’ reasoning capacities.

Since 2001, unprecedented resources have been invested in research into global terrorism, resulting in a dramatic rise in the number of academic publications on the topic. Works by scholars from predominantly quantitative disciplines predominate in this literature, and the unfolding development of data science and big data research has accentuated the trend. Many researchers in global terrorism created event databases, in which every row represents a distinct terrorist attack and every column a variable (e.g., the date and location of the attack, the number of casualties, etc.). Such event data are usually extracted from news sources and undergo a process of coding—the translation of unstructured text into numerical or categorical values. Some researchers collect and code their data manually; others use an automated script, or combine the efforts of humans and software. Other researchers who use event data do not collect and process their data at all; rather, they analyze other scholars’ databases. Academics and practitioners have relied on such databases for the cross-regional study of terrorism, analyzing their data statistically in an attempt to identify trends, build theories, predict future incidents, and formulate policies. Unfortunately, event data on terrorism often suffer from substantial issues of accuracy and reproducibility. A comparison between the data on suicide terrorism in Israel and the occupied Palestinian territories in two of the most prominent databases in the field and an independent database of confirmed events reveals the magnitude of these problems. Among the most common pitfalls for event data are replication problems (the sources that the databases cite, if there are any at all, cannot be retrieved), selection bias (events that should have been included in the database are not in it), description bias (the details of events in the database are incorrect), and coding problems (for example, duplicate events). Some of these problems originate in the press sources that are used to create the databases, usually English-language newspaper articles, and others are attributable to deficient data-gathering and/or coding practices on the part of database creators and coders. In many cases, these researchers do not understand the local contexts, languages, histories, and cultures of the regions they study. Further, many coders are not trained in qualitative methods and are thus incapable of critically reading and accurately coding their unstructured sources. Overcoming these challenges will require a change of attitude: truly accurate and impactful cross-regional data on terrorism can only be achieved through collaboration across projects, disciplines, and fields of expertise. The creators of event databases are encouraged to adopt the high standards of transparency, replicability, data-sharing, and version control that are prevalent in the STEM sciences and among software developers. More than anything, they need to acknowledge that without good and rigorous qualitative work during the stage of data collection, there can be no good quantitative work during the stage of data analysis.

Individuals’ political membership of and identification with their political parties or ideologies influence how they interact with members of their own group/party (ingroup) and other groups/parties (outgroups). Such sense of “group-ness” (i.e., us-them) motivates people to find ways to posit their group in a positive light (i.e., ingroup favoritism) and engage in attitudes and behaviors that help maintain a desired political group status. These attitudes and behaviors can include a positively biased attitude toward one’s own political group over the other groups, a tendency to seek information that confirms the viewpoints and reflects positive aspects of one’s own political group, or a perception that the media exhibit bias toward their political group. Hence, we can consider political activities, attitudes, and communication (including media use) as inherently intergroup behaviors where members of political groups constantly appraise political discourse and political landscape through an intergroup lens. These intergroup phenomena are particularly salient during election seasons where political group boundaries are erected and political discourses around core ideological beliefs are debated. Accordingly, it is important to understand better links between intergroup factors (e.g., intergroup attitudes and behaviors), and political media use. This requires: (a) examining how intergroup factors have been considered in political communication research; (b) assessing political media use and effects in a political context, specifically media effects that could potentially be driven by political group identity and political intergroup attitudes; (c) discussing studies that have highlighted intergroup variables in the process of media selection and effects and how we may conceptualize political media research in an intergroup framework; and (d) considering additional intergroup factors that might be relevant and informative to our understanding of political activities and attitudes and media selection and effects in political settings.

Harold Innis is one of the foundational theorists of media and communications studies. In the mid-20th century, he developed his concept of media bias (also called the bias of communication). It remains Innis’s most cited concept, but it is also significantly misunderstood. For example, since his death in 1952, bias has often been applied in ways that are akin to a form of technological or media determinism. This has been an ongoing problem despite the fact that Innis developed his concept as a means of compelling analysts to reject such mechanistic formulations. Indeed, his goal was to promote more self-reflective modes of scholarship and, by extension, a recognition that such intellectual capacities—which he believed were essential for civilization’s survival—would be lost if they were not recognized and defended. More generally, Innis contextualized his work regarding media bias in terms of interrelated historical conditions involving political economic dynamics. Through his application of the concept to over 4000 years of history, he sought to provide his contemporaries with the reflective perspective needed to comprehend the underpinnings of modern biases that stressed present-mindedness and spatial control to the neglect of creativity and duration. Bias was derived from Innis’s studies on Canadian economic development involving the exploitation of its resources (an approach to history called the staples thesis). Several of the insights he garnered in those studies need to be recognized if we are to fully understand his subsequent communications research. Also, tracing the origins of his concept of bias enables us to fully assess the nature of Innis’s supposed media determinism. Typical uses of bias today focus on the spatial or temporal orientations that many assume are compelled through the use of a specific medium or set of media technologies. This misreading, inspired mainly by Marshall McLuhan’s representations of Innis, has led to assumptions regarding Innis’s determinism and a general neglect of the complexity of his original work. To repeat, Innis developed bias in order to redress the mechanistic and unreflective thinking of his day and always conceptualized it in terms of factors that are salient to the place and time being examined. Moreover, he applied bias alongside now largely forgotten concepts ranging from unused capacity to classic power–knowledge dialectics. Lastly, he situated the development and implications of a particular medium in relation to both old and new media (not just technologies, but organizations and institutions also). In sum, to comprehend Innis’s concept of bias, its intellectual and political underpinnings need to be acknowledged, the political economic dynamics of its development and application understood, and the implications of McLuhan’s influence recognized.