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Record W2110081343 · doi:10.1002/ejsp.863

Threat(s) and conformity deconstructed: Perceived threat of infectious disease and its implications for conformist attitudes and behavior

2011· article· en· W2110081343 on OpenAlexaff
Damian R. Murray, Mark Schaller

Bibliographic record

VenueEuropean Journal of Social Psychology · 2011
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConformistConformityPsychologySocial psychologySalience (neuroscience)Normative social influenceMortality salienceNormativeAttributionSocial norms approachCognitive psychologyPerception

Abstract

fetched live from OpenAlex

Abstract Threat has been linked to conformity, but little is known about the specific effects of different kinds of threat. We test the hypothesis that perceived threat of infectious disease exerts a unique influence on conformist attitudes and behavior. Correlational and experimental results support the hypothesis. Individual differences in Perceived Vulnerability to Disease predict conformist attitudes; these effects persist when controlling for individual differences in the Belief in a Dangerous World. Experimentally manipulated salience of disease threat produced stronger conformist attitudes and behavior, compared with control conditions (including a condition in which disease‐irrelevant threats were salient). Additional results suggest that these effects may be especially pronounced in specific domains of normative behavior that are especially pertinent to pathogen transmission. These results have implications for understanding the antecedents of conformity, the psychology of threat, and the social consequences of infectious disease. Copyright © 2011 John Wiley & Sons, Ltd.

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)0.008
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.000
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 head0.141
GPT teacher head0.350
Teacher spread0.209 · 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

Citations252
Published2011
Admission routes1
Has abstractyes

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