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Record W2098526396 · doi:10.1177/0146167204271177

Perspective and Prejudice: Antecedents and Mediating Mechanisms

2004· article· en· W2098526396 on OpenAlexaff
John F. Dovidio, Marleen ten Vergert, Tracie L. Stewart, Samuel L. Gaertner, James D. Johnson, Victoria M. Esses, Blake M. Riek, Adam R. Pearson

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsWestern University
FundersNational Institute of Mental Health
KeywordsPrejudice (legal term)PsychologyFeelingSocial psychologyInjusticeIngroups and outgroupsPerspective (graphical)PerceptionSocial identity theoryIdentity (music)Social group

Abstract

fetched live from OpenAlex

The present work investigated mechanisms by which Whites' prejudice toward Blacks can be reduced (Study 1) and explored how creating a common ingroup identity can reduce prejudice by promoting these processes (Study 2). In Study 1, White participants who viewed a videotape depicting examples of racial discrimination and who imagined the victim's feelings showed greater decreases in prejudice toward Blacks than did those in the objective and no instruction conditions. Among the potential mediating affective and cognitive variables examined, reductions in prejudice were mediated primarily by feelings associated with perceived injustice. In Study 2, an intervention designed to increase perceptions of a common group identity before viewing the videotape, reading that a terrorist threat was directed at all Americans versus directed just at White Americans, also reduced prejudice toward Blacks through increases in feelings of injustice.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.034
GPT teacher head0.378
Teacher spread0.344 · 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

Citations391
Published2004
Admission routes1
Has abstractyes

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