Crossing Status Divides:
Stereotypes, Strategies, and Solutions

“This is not who we are.” Summer 2020 saw millions march for racial equity; fall brought an American election deemed stolen by many; winter opened with an invasion of the U.S. Capital and Brexit. All the while, this phrase pervaded social media, public statements, and commentaries. Intergroup conflict, however, has long plagued humankind; this conflict is central to the struggle to increase diversity in organizations and reduce social inequality. Using interdisciplinary approaches grounded in sound theory, my research illuminates how stereotypes are both a source of and solution to group conflicts. As a social scientist, I operate at the nexus of social cognition, political psychology, and organizational behavior to advance our understanding of how people navigate social inequality.

Among the core insights of psychology is that people, whether in the coffee shop or the boardroom, in person or online, need to feel accepted. They need to feel that people trust and respect them. But stereotypes—inferred traits of social groups—are often ambivalent, and can hinder intergroup contact. For example, when scientists are viewed by the public as competent, but cold, these stereotypes can undermine trust in science during consequential policy discussions (Fiske & Dupree, 2014, PNAS).

My research investigates the central role of stereotypes in reproducing and upending societal inequities. I study (1) how people manage stereotypes, (2) how stereotypes maintain inequality, and (3) how we might use our knowledge about stereotypes to design interventions that mitigate conflict.

Characterizing Intergroup Interactions Through Stereotype Reversal

People are aware of the negative stereotypes applied to their groups, such as those depicting Black Americans as low-status and White women as submissive. These stereotypes can reduce the desire for intergroup contact. Stereotypes may also impact how people present themselves in intergroup settings, but little work has examined stereotyping and self-presentation in tandem. My research meets this goal.

In a social-cognitive theory of how people contend with stereotypes in intergroup interactions (Dupree, in preparation; Fiske, Dupree, et al., 2016, COP; Swencionis, Dupree, & Fiske, 2017, JSI), I assert that people attempt to reverse stereotypes using language. My theory consists of three tenets: (1) people are aware of negative stereotypes applied to their group, (2) when seeking acceptance in outgroup settings, people use language to reverse stereotypes; and (3) such language can predict how people are seen and whether they are accepted, revealing who gets along and who gets ahead. I test this theory in the field and lab, using varied methodologies that allow for the unobtrusive observation of behavior and manifold populations, because I care about centering the voices of people from groups that are often disregarded (Dupree, 2021, PI; Dupree & Boykin, 2021, PIBBS; Dupree & Kraus, 2021, PoPS).

Stereotype Reversal among White Liberals. I first tested this theory among White liberals, who tend to be interested in affiliating with racial minorities. I expected that White liberals would present less competence when speaking to a Black (vs. White) audience. This behavior, I reasoned, would be rooted in attempts to reverse stereotypes that depict them as dominant and to smooth interracial interactions that can be fraught with anxiety. I sampled laypeople and presidential candidates, using controlled lab and field observation techniques. As predicted, White liberals tended to use words that make one appear less competent (e.g., “pretty” instead of “exquisite”) and use fewer words related to competence (e.g., “competitive”) when addressing Black (versus White) people in a work task, during a casual introduction, or on the campaign trail. White liberals reverse stereotypes about their group in a likely well-intentioned, if patronizing, attempt to affiliate (Dupree & Fiske, 2019, JPSP).

Stereotype Reversal among Minority Conservatives. I next turned to racial minorities, in some of the first research to document minorities’ interracial self-presentation. Political psychology suggests that minority conservatives tend to distance themselves from their racial ingroup. I thus predicted that, when addressing White audiences, minority conservatives reverse stereotypes, presenting more competence than liberals. Using natural language processing, I analyzed the stereotype content of 250,000 Congressional remarks, nearly 1,000,000 tweets, and 1,200 introductions by Black, Latinx, and White legislators and laypeople. Black and Latinx conservatives referenced competence more than liberals, using more words like “assertive” or “influential” (Dupree, 2021, Nature Human Behavior).

Stereotype Reversal among Women. In a third test of my theory, I focused on gender. Management scholars have long examined gender disparities in leaders’ com- munication and followers’ reactions. There is, however, a paucity of research that takes an intersectional perspective. I took that step, using an intersectional lens to examine women leaders’ use of dominant language and how others receive it. Leveraging advances in natural-language processing, I analyzed the stereotype content of more than 250,000 Congressional remarks (Study 1) and almost one million tweets (Study 2) by leaders. Women leaders referenced dominance more than men did (using more words like ‘‘powerful’’), violating stereotypes that depict women as submissive. However, as theory on racialized gender stereotypes suggests, this effect was unique to White leaders. Analyzing 18,000 editorials revealed backlash from the media: the more women leaders referenced dominance, the more journalists portrayed them using dominant, but cold (“unfriendly”, “unlikeable”) language. This backlash effect was strongest for Black and Latina women. Finally, voters also showed backlash, unique to Black women. The more Black women leaders used dominant language in a simulated social media profile, the more voters rated them as unlikeable (Dupree, 2024, ASQ).

Stereotypes Fuel Inequality

By 2050, racial minorities will compose over 50% of the American population. However, as evidenced by the lack of racial diversity in higher education, Fortune 500 boards, government officials, and newsrooms, diversity efforts in organizations struggle to gain traction. My secondary research characterizes how stereotypes—about jobs (Fiske & Dupree, 2014, PNAS), status (Dupree, Torrez, Obioha, & Fiske, 2021, JPSP), or ideology (Dupree & Torrez, 2021, JESP)—can reify hierarchies.

Race-Status Associations. Research and anecdotal evidence (e.g., while senator, Barack Obama was often mistaken for a waiter) suggests that people associate “Black” with “poor” and “White” with “rich”. I examined the emergence, meaning, and consequences of race-status associations (RSAs) for job-seekers and policy preferences. I developed three novel RSA measures, validating them among 4,000 White and Black Americans. For White Americans, a jobs-based measure—assigning White people to high-status jobs (e.g. “doctor”) and Black people to low-status jobs (e.g., “janitor”)—predicted rejection of Black applicants and opposition to equalizing policies. This indirect indicator of RSAs predicted preferences that reify inequality, suggesting endorsement. In contrast, for White and Black Americans, rank- or attribute-based measures—ranking White Americans as higher-status or higher on status-relevant attributes (e.g., “wealthy”) than Black Americans—predicted support for equalizing policies. Direct acknowledgement of structural inequality predicted preferences to undo it. Measurement matters, as does perceivers’ race (Dupree, Torrez, Obioha, & Fiske, 2021, JPSP).

I will continue to advance the studies of racial and socio-economic inequality by empirically examining their interplay. For instance, we find that status-based hiring discrimination depends on applicant race: Black low- (versus high-) status applicants are rejected for high-status jobs, but this pattern flips for White applicants (Torrez & Dupree, in preparation). I will also further probe the forms and consequences of RSAs, examining single category RSAs (e.g., Black = low status vs. White = high status effects), RSAs toward and among different racial (e.g., Asian Americans) and stigmatized (e.g., LGBTQ) groups, and related stereotypes such as power and ability.

Reducing Inequalities in Interactions and Society

Finally, I test theoretically-grounded interventions that can bridge group divides in social interactions and society writ large. This intervention work illuminates practical applications of social science to improve intergroup contact and mitigate social inequality, while making critical contributions to organizational and social psychological theories of group dynamics.

Conversations about Inequality. To combat social inequality, people must first be aware of it. We tested three interventions to improve people’s understanding of the racial wealth gap. Community members watched a video about a family contending with obstacles exacerbated due to this gap, about data-based trends related to this gap, or about the narrative and data. Participants estimated the racial wealth gap before, directly after, and 18 months after the intervention. Data-based interventions most improved estimates over time (Callaghan, Harouni, Dupree, Kraus, & Richeson, 2021, PNAS).  

Summer 2020 revealed the power of public conversations around inequality. Upcoming work will illuminate how people talk about inequality (e.g., as interpersonal or systemic) and how speakers’ race, gender, or ideology predict the content of these discussions. For one project, we collected 500,000 #BlackLivesMatter tweets, employed facial recognition software to estimate users’ race and gender, and are currently estimating  users’ ideology with latent space modeling based on the political accounts that users follow (Dupree, Harouni, & Callaghan, data collection). Related studies explore mentors discuss race in the workplace (Sanchez & Dupree, data collection).

Quelling Anxiety. Anxiety may play a key role as source of and solution to stereotype reversal in intergroup settings. In a series of lab studies, Kiara Sanchez and I are testing whether and how White liberals “downshift competence” toward Black (vs. White) partners face-to-face, testing verbal behavior, nonverbal behavior, and physiological stress responses before employing interventions that can directly quell anxiety (by normalizing discomfort or activating a growth mindset) or indirectly do so (by dampening stereotype activation).

Conclusions

We are in the midst of an ongoing struggle over inequality, the likes of which we have rarely seen in modern times. However, intergroup conflict is nearly as old as humanity itself; it is “who we are”. In an increasingly diverse and polarized society, interacting with members of different social groups is unavoidable—and can either perpetuate or mitigate group conflict. Using surveys, experiments, and field studies across the lab, workplaces, and social media, my research program reveals the stereotypes that can maintain group divides—and the strategies people use to overcome them.