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Record W4323667910 · doi:10.1002/ejsp.2938

Ideologically‐based contact avoidance during a pandemic: Blunt or selective distancing from ‘others’?

2023· article· en· W4323667910 on OpenAlexaff
Gordon Hodson, Rose Meleady

Bibliographic record

VenueEuropean Journal of Social Psychology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsBrock University
FundersLeverhulme Trust
KeywordsIngroups and outgroupsSocial dominance orientationPsychologyIdeologyOutgroupSocial psychologyDominance (genetics)Social distanceBiology and political orientationAuthoritarianismDevelopmental psychologyPoliticsCoronavirus disease 2019 (COVID-19)Political scienceMedicine

Abstract

fetched live from OpenAlex

Abstract This project sought to understand when ideology is relevant (or not) to predicting contact avoidance of ‘others’ during the COVID‐19 pandemic. Right‐leaning ideologies (political conservatism, right‐wing authoritarianism, social dominance orientation) were not expected to predict greater contact avoidance per se, but rather exhibit selective avoidance of outgroup (vs. ingroup) members. White British participated in one exploratory (Study 1 N = 364) and two pre‐registered (Study 2 N = 431, Study 3 N = 700) studies. As expected, right‐leaning ideologies were significantly stronger predictors of greater preferred personal distance and contact discomfort regarding foreign outgroups (vs. British ingroup) in Studies 1 and 3 (partially supported in Study 2). Ideology rarely predicted ingroup reactions. This Ideology × Target pattern was itself not moderated by the perceived COVID‐19 threat. Pre‐pandemic theorizing that heightened behavioural immune system responses are associated with heightened right‐leaning ideologies appear insufficient for use in actual pandemic contexts, especially when highly politicized.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

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

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 head0.147
GPT teacher head0.343
Teacher spread0.196 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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