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Record W2119454372 · doi:10.1177/0956797611417261

Immunizing Against Prejudice

2011· article· en· W2119454372 on OpenAlexaff
Julie Y. Huang, Alexandra Sedlovskaya, Joshua M. Ackerman, John A. Bargh

Bibliographic record

VenuePsychological Science · 2011
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrejudice (legal term)PsychologySocial psychology

Abstract

fetched live from OpenAlex

Contemporary interpersonal biases are partially derived from psychological mechanisms that evolved to protect people against the threat of contagious disease. This behavioral immune system effectively promotes disease avoidance but also results in an overgeneralized prejudice toward people who are not legitimate carriers of disease. In three studies, we tested whether experiences with two modern forms of disease protection (vaccination and hand washing) attenuate the relationship between concerns about disease and prejudice against out-groups. Study 1 demonstrated that when threatened with disease, vaccinated participants exhibited less prejudice toward immigrants than unvaccinated participants did. In Study 2, we found that framing vaccination messages in terms of immunity eliminated the relationship between chronic germ aversion and prejudice. In Study 3, we directly manipulated participants' protection from disease by having some participants wash their hands and found that this intervention significantly influenced participants' perceptions of out-group members. Our research suggests that public-health interventions can benefit society in areas beyond immediate health-related domains by informing novel, modern remedies for prejudice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.334
GPT teacher head0.355
Teacher spread0.021 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations152
Published2011
Admission routes1
Has abstractyes

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