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Record W4416765245 · doi:10.3390/covid5120195

The Class Gap in Pandemic Attitudes and Experiences

2025· article· en· W4416765245 on OpenAlexafffund
Claus Rinner

Bibliographic record

VenueCOVID · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsToronto Metropolitan University
FundersGovernment of Canada
KeywordsPandemicOpposition (politics)Government (linguistics)Coronavirus disease 2019 (COVID-19)Social distanceWorld classSocial classPublic health

Abstract

fetched live from OpenAlex

Attitudes towards COVID-19 and lived experiences during the pandemic depended greatly on people’s level of education. This study extends a previous analysis of vaccine hesitancy as a function of formal education and examines additional indicators from the COVID-19 Trends and Impacts Survey for the United States during 2021–2022. The monthly values for social and health-related activities and constraints, testing and vaccination decisions, and information-seeking behaviours, as well as trust and beliefs, often varied markedly between education-defined classes. Many indicators present a significant gap between the attitudes and experiences of better-educated groups, represented by college/university graduates and those with post-graduate studies, on the one hand, and less-educated groups, including those with only high school or some college education, on the other hand. These patterns suggest that the academic and professional-managerial classes, which supply the vast majority of societal decision-makers, may be ill-equipped to understand and respect the needs and worries of the working class in an emergency situation such as the COVID-19 pandemic. Given growing concerns about the benefit–harm balance of many government policies, a more inclusive pandemic response could have been achieved by respecting and adopting the common sense, scepticism, and outright opposition of the less-educated groups vis-a-vis restrictions and public health measures.

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)0.000
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 score0.533
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.363
Teacher spread0.328 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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