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Record W2313759460 · doi:10.1177/1948550616639651

Can a Naturally Occurring Pathogen Threat Change Social Attitudes? Evaluations of Gay Men and Lesbians During the 2014 Ebola Epidemic

2016· article· en· W2313759460 on OpenAlexaff
Yoel Inbar, Erin Corwin Westgate, David A. Pizarro, Brian A. Nosek

Bibliographic record

VenueSocial Psychological and Personality Science · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySocial psychologyEbola virusHomosexualityOutbreakVirologyMedicine

Abstract

fetched live from OpenAlex

Previous evidence linking disease threat and social attitudes suggests that a highly salient society-wide pathogen threat should lead to more negative attitudes toward gay men and lesbians. Using a sample of 248,922 Americans recruited via the Project Implicit website, we tested whether implicit attitudes toward gay men and lesbians shifted as a result of the 2014 Ebola virus outbreak. Regression discontinuity analyses, but not t-tests, showed evidence of a small shift in implicit (but not explicit) attitudes at the height of public concern over Ebola. These results could be interpreted as providing partial support for the effects of naturally occurring pathogen threats on social attitudes. Alternatively, given the large size of our sample, the mixed evidence and small effects may reflect a boundary condition for the operation of the behavioral immune system.

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)0.007
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.296
GPT teacher head0.416
Teacher spread0.121 · 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

Citations44
Published2016
Admission routes1
Has abstractyes

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