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Racialized Perceptions of Vegetarianism: Stereotypical Associations That Undermine Inclusion in Eating Behaviors
Daniel Rosenfeld, Tiffany Brannon & Janet Tomiyama
Personality and Social Psychology Bulletin, forthcoming
Abstract:
Shifting societal eating patterns toward a vegetarian diet offers promise for improving public health and environmental sustainability. Yet concerns exist about racial disparities in inclusion, as some sentiments suggest that vegetarianism is stereotypically associated with Whiteness. Through four studies (total N = 3,234), we investigated associations U.S. adults hold between race and vegetarianism, along with implications for behavior change and belongingness among Black individuals. Participants, across racial backgrounds, strongly associated vegetarianism with Whiteness, both explicitly and implicitly. A race prime led Black participants to report lower interest in becoming a vegetarian, whereas a prime of race-vegetarianism associations decreased Black participants' feelings of belongingness in the vegetarian community. Exposure to racially inclusive messaging about vegetarianism, meanwhile, increased belongingness among Black participants. These findings provide the first quantitative insights into racial stereotypes about vegetarianism and pose future directions for theory, research, and practice at the intersections of race and eating behavior.
Hate, amplified? Social media news consumption and support for anti-Muslim policies
Nazita Lajevardi, Kassra Oskooii & Hannah Walker
Journal of Public Policy, forthcoming
Abstract:
Research finds that social media platforms' peer-to-peer structures shape the public discourse and increase citizens' likelihood of exposure to unregulated, false, and prejudicial content. Here, we test whether self-reported reliance on social media as a primary news source is linked to racialised policy support, taking the case of United States Muslims, a publicly visible but understudied group about whom significant false and prejudicial content is abundant on these platforms. Drawing on three original surveys and the Nationscape dataset, we find a strong and consistent association between reliance on social media and support for a range of anti-Muslim policies. Importantly, reliance on social media is linked to policy attitudes across the partisan divide and for individuals who reported holding positive or negative feelings towards Muslims. These findings highlight the need for further investigation into the political ramification of information presented on contemporary social media outlets, particularly information related to stigmatised groups.
Associations between provider-assigned Apgar score and neonatal race
Sara Edwards et al.
American Journal of Obstetrics and Gynecology, forthcoming
Study Design:
We conducted a retrospective cohort study at an urban academic medical center. All live births at 23 weeks and 0 days gestational age and greater from January 1, 2019 through December 31, 2019 with complete data available were included. Data were queried from the electronic medical record and included race, ethnicity, and gestational age of neonate, as well as umbilical cord gas values (umbilical artery pH and base deficit), admission to Neonatal Intensive Care Unit, and presence of maternal-fetal complications. Primary outcome measures were neonates' Apgar scores at 1 and 5 minutes. Color Apgar score and admission to Neonatal Intensive Care Unit served as secondary outcome measures. We performed three partially proportional ordinal regression models controlling for an increasing number of covariates, with Model 1, the baseline model, adjusted for gestational age, Model 2 additionally adjusted for umbilical cord gases, and Model 3 additionally adjusted for maternal medical conditions and pregnancy complications.
Results:
977 neonates met selection criteria; 553 (56.6%) were Black. Providers assigned Black neonates significantly lower Apgar scores at 1 minute (OR=0.63, 95% CI=0.49-0.80) and 5 minutes (OR=0.64, 95% CI=0.47-0.87), when controlling for umbilical artery gases, gestational age, and maternal-fetal complications. This difference appeared related to significantly lower assigned color Apgar scores at 1 minute when controlling for all above factors (OR=0.52, 95% CI=0.39-0.68). Providers admitted full-term Black neonates to the Neonatal Intensive Care Unit at higher odds than non-Black neonates when controlling for all factors (OR=1.29, 95% CI=0.94-1.77). Black neonates did not have more abnormal cord gas values (mean umbilical artery pH 7.259 for Black neonates vs. 7.256 for non-Black neonates), which would have supported their admission to the Neonatal Intensive Care Unit.
Scapegoating and Discrimination in Times of Crisis: Evidence from Airbnb
Michael Luca, Elizaveta Pronkina & Michelangelo Rossi
NBER Working Paper, August 2022
Abstract:
We present evidence that discrimination against Asian-American Airbnb users sharply increased at the start of the COVID-19 pandemic. Using a DiD approach, we find that hosts with distinctively Asian names experienced a 12 percent decline in guests relative to hosts with distinctively White names. In contrast, we do not see spikes in discrimination against Black or Hispanic hosts. Our results suggest that the rise in anti-Asian sentiment in 2020 translated to discrimination in economic activity, highlighting the ways in which scapegoating minority groups can shape markets. Our results also point to the role of platform design choices in enabling discrimination.
Pathogen threat and intergroup prejudice using the minimal group paradigm: Evidence from a registered report
Anastasia Makhanova et al.
Evolution and Human Behavior, forthcoming
Abstract:
Infection by parasites, bacteria, and other microorganisms has been a powerful selection pressure faced by humans and other species. Consequently, avoiding pathogens has played an important role in human evolution and continues to play a role in contemporary social psychological processes. The current research tested the hypothesis that pathogen avoidance promotes intergroup prejudice. Whereas previous tests relied on existing cultural groups, which can conflate outgroup status with pre-existing group stereotypes about disease or geographic variability in pathogen prevalence, the current experiments assessed intergroup bias using a minimal group paradigm. Based on preliminary evidence (Study 1, N = 207, undergraduate students) that experimentally priming pathogen avoidance motivation promoted negativity toward a minimal outgroup, we conducted a Registered Report (Study 2, N = 1339 online participants) to replicate and extend those findings. Our primary hypothesis was that an experimental manipulation of pathogen threat (relative to two control conditions) would produce greater intergroup prejudice (negativity toward the nominal outgroup relative to the nominal ingroup). This hypothesis was not supported in the larger, registered second study. Exploratory analyses provided some evidence for interactions between experimental priming of pathogen threat and individual differences in pathogen disgust, but the interactive pattern differed across the two experiments. Findings call into question the hypothesis that, in the absence of cultural stereotypes, situationally activated pathogen avoidance promotes intergroup prejudice.
Patterns of Implicit and Explicit Attitudes: IV. Change and Stability From 2007 to 2020
Tessa Charlesworth & Mahzarin Banaji
Psychological Science, forthcoming
Abstract:
Using more than 7.1 million implicit and explicit attitude tests drawn from U.S. participants to the Project Implicit website, we examined long-term trends across 14 years (2007-2020). Despite tumultuous sociopolitical events, trends from 2017 to 2020 persisted largely as forecasted from past data (2007-2016). Since 2007, all explicit attitudes decreased in bias between 22% (age attitudes) and 98% (race attitudes). Implicit sexuality, race, and skin-tone attitudes also continued to decrease in bias, by 65%, 26%, and 25%, respectively. Implicit age, disability, and body-weight attitudes, however, continued to show little to no long-term change. Patterns of change and stability were generally consistent across demographic groups (e.g., men and women), indicating widespread, macrolevel change. Ultimately, the data magnify evidence that (some) implicit attitudes reveal persistent, long-term change toward neutrality. The data also newly reveal the potential for short-term influence from sociopolitical events that temporarily disrupt progress toward neutrality, although attitudes eventually return to long-term homeostasis in trends.
Untested assumptions perpetuate stereotyping: Learning in the absence of evidence
William Cox, Xizhou Xie & Patricia Devine
Journal of Experimental Social Psychology, forthcoming
Abstract:
In the present work, we set out to assess whether and how much people learn in response to their stereotypic assumptions being confirmed, being disconfirmed, or remaining untested. In Study 1, participants made a series of judgments that could be influenced by stereotypes and received feedback that confirmed stereotypes the majority of the time, feedback that disconfirmed stereotypes the majority of the time, or no feedback on their judgments. Replicating past work on confirmation bias, patterns in the conditions with feedback indicated that pieces of stereotype-confirming evidence exerted more influence than stereotype-disconfirming evidence. Participants in the Stereotype-Confirming condition stereotyped more over time, but rates of stereotyping for participants in the Stereotype-Disconfirming condition remained unchanged. Participants who received no feedback, and thus no evidence, stereotyped more over time, indicating that, matching our core hypothesis, they learned from their own untested assumptions. Study 2 provided a direct replication of Study 1. In Study 3, we extended our assessment to memory. Participants made judgments and received a mix of confirmatory, disconfirmatory, and no feedback and were subsequently asked to remember the feedback they received on each trial, if any. Memory tests for the no feedback trials revealed that participants often misremembered that their untested assumptions were confirmed. Supplementing null hypothesis significance testing, Bayes Factor analyses indicated the data in Studies 1, 2, and 3 provided moderate-to-extreme evidence in favor of our hypotheses. Discussion focuses on the challenges these learning patterns create for efforts to reduce stereotyping.
I Am Not A Virus: Status-Based Rejection Sensitivity and Sleep Among East Asian People in the United States During COVID-19
Doris Dai & Cynthia Levine
Social Psychological and Personality Science, forthcoming
Abstract:
As COVID-19 spread in the United States, anti-East Asian bias increased. This article aimed to (1) show that thinking about COVID-19 heightened East Asian individuals' anxious expectations of discrimination and (2) explore these expectations' health correlates. Specifically, the paper focused on COVID-19-triggered race-based rejection sensitivity, defined as (1) East Asian individuals' expectations of rejection due to the stereotype that they spread the virus and (2) high levels of anxiety about this possibility. Study 1 (N = 412) showed that reminders of COVID-19 increased COVID-19-triggered race-based rejection sensitivity among Chinese citizens living in the United States and East Asian Americans, but not Americans of other races. Study 2 (N = 473) demonstrated that East Asian people who habitually focused on COVID-19 experienced greater COVID-19-triggered race-based rejection sensitivity and, in turn, greater sleep difficulties. Thus, societal-level shifts that target minoritized groups may increase minoritized group members' concerns about discrimination in ways that undermine their health.
Who gets canceled? Twitter responses to gender-based violence allegations
Alyssa Glace Maryn & Tessa Dover
Psychology of Violence, forthcoming
Method:
We analyzed the sentiment of a large sample of tweets (N = 182,456) about a sample of GBV allegations (N = 120) using sentiment analysis software (i.e., linguistic inquiry and word count; LIWC). Using multilevel regression, we assessed changes in tweet positive and negative emotion words over time. We also assessed differences in these outcomes based on the age of the accuser and the race and fame of the accused.
Results:
Tweets about White and Black alleged perpetrators of GBV included more positive emotion over time. Additionally, tweets discussing Black (vs. White) alleged perpetrators were less positive while tweets discussing non-Black alleged perpetrators of color (vs. White) were more positive. Tweets discussing face famous (i.e., recognizable to those who consume their work; vs. nonface famous) alleged perpetrators were discussed with higher levels of both positive and negative emotion.
Measuring Representation of Race, Gender, and Age in Children's Books: Face Detection and Feature Classification in Illustrated Images
Teodora Szasz et al.
University of Chicago Working Paper, May 2022
Abstract:
Images in children's books convey messages about society and the roles that people play in it. Understanding these messages requires systematic measurement of who is represented. Computer vision face detection tools can provide such measurements; however, state-of-the-art face detection models were trained with photographs, and 80% of images in children's books are illustrated; thus existing methods both misclassify and miss classifying many faces. In this paper, we introduce a new approach to analyze images using AI tools, resulting in data that can assess representation of race, gender, and age in both illustrations and photographs in children's books. We make four primary contributions to the fields of deep learning and social sciences: (1) We curate an original face detection data set (IllusFace 1.0) by manually labeling 5,403 illustrated faces with bounding boxes. (2) We train two AutoML-based face detection models for illustrations: (i) using IllusFace 1.0 (FDAI); (ii) using iCartoon, a publicly available data set (FDAI iC), each optimized for illustrated images, detecting 2.5 times more faces in our testing data than the established face detector using Google Vision (FDGV). (3) We curate a data set of the race, gender, and age of 980 faces manually labeled by three different raters (CBFeatures 1.0). (4) We train an AutoML feature classification model (FCA) using CBFeatures 1.0. We compare FCA with the performance of another AutoML model that we trained on UTKFace, a public data set (FCA UTK) and of an established model using FairFace (FCF). Finally, we examine distributions of character identities over the last century across the models. We find that FCA is 34% more accurate than FCF in its race predictions. These contributions provide tools to educators, caregivers, and curriculum developers to assess the representation contained in children's content.
Racial discrimination in non-fungible token (NFT) prices? CryptoPunk sales and skin tone
Jeremy Nguyen
Economics Letters, forthcoming
Abstract:
Using sales data for the non-fungible token (NFT) collection titled 'CryptoPunks' (June 2017 - October 2021, , 883), we examine whether certain skin tones of the artworks trade at different prices. Results indicate that CryptoPunks with lighter skin tones (Albino and Light), trade at significantly higher prices. CryptoPunks with Dark skin trade at lower prices, even after controlling for rarity and market conditions.