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Record W4405319593 · doi:10.1037/pspa0000433

Prejudice and stereotypes at regional and individual levels: Related but distinct.

2024· article· en· W4405319593 on OpenAlexafffund
Jennifer Suliteanu, Eugene K. Ofosu, Eric Hehman

Bibliographic record

VenueJournal of Personality and Social Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrejudice (legal term)PsycINFOPsychologySocial psychologyStereotype (UML)Construct (python library)Social perceptionVariation (astronomy)Similarity (geometry)PerceptionMEDLINE

Abstract

fetched live from OpenAlex

Exploring how psychological constructs and their outcomes vary across geographic regions is a rapidly expanding area of research, yet fundamental questions remain. Can constructs designed to describe individual variation in attitudes be interpreted in the same way when aggregated to regional levels? To what extent are they related or distinct? We tested the relationship between individual and regional attitudes across four studies in the domain of intergroup attitudes. Participants reported explicit prejudices and stereotypes toward 14 different social groups, and incorporating data from Project Implicit, we compared the characteristics of regional and individual operationalizations of prejudice. Further, we tested whether attitudes related to one another in the same way across levels using representational similarity analysis. Drawing from construct validity theory, we find evidence that regional prejudice is an emergent property of individual attitudes, to which it is related but distinct. These findings contextualize stereotype and prejudice constructs in regional analyses in psychology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.238
GPT teacher head0.428
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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