Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".