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Record W4244067872 · doi:10.31219/osf.io/agm57

Anti-Intellectualism and the Mass Public’s Response to the Covid-19 Pandemic

2020· preprint· en· W4244067872 on OpenAlexaff
Eric Merkley, Peter John Loewen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntellectualismDistrustSocial distancePublic relationsCoronavirus disease 2019 (COVID-19)PandemicPolitical sciencePsychologySocial psychologyLawMedicine

Abstract

fetched live from OpenAlex

Anti-intellectualism – the generalized distrust of experts and intellectuals – is an important concept in explaining the public’s engagement with advice from scientists and experts. We ask whether it has shaped the mass public’s response to COVID-19. We provide evidence of a consistent connection between anti-intellectualism and COVID-19 risk perceptions, social distancing, mask usage, misperceptions, and information acquisition using a representative survey of 27,615 Canadians conducted from March to July 2020. We exploit a panel-component of our design (N=4,910) to strongly link anti-intellectualism and within-respondent change in mask usage. Finally, we provide experimental evidence of anti-intellectualism’s importance in information search behaviour with two conjoint studies (N~2,500) that show respondents’ preferences for COVID-19 news and COVID-19 information from experts dissipate among those with higher levels of anti-intellectual sentiment. Anti-intellectualism poses a fundamental challenge in maintaining and increasing public compliance with expert-guided COVID-19 health directives.

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)0.008
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.179
GPT teacher head0.389
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations22
Published2020
Admission routes1
Has abstractyes

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