The enforcement of statewide mask wearing mandates to prevent COVID-19 in the US: an overview
Bibliographic record
Abstract
Face masks have become the bulwark of COVID-19 prevention in the US. Between 10 April and 1 August, 2020, 33 state governors issued orders requiring businesses to require their customers and employees to wear face masks, and persons outdoors who could not social distance to do the same. We documented the policies and enforcement actions for these policies in each of the states. We used governors' orders and journalists' news reports as our sources. Our results show that the states used a variety of state and local (county and municipality) agencies to enforce business prevention behaviors and primarily local law enforcement agencies to enforce outside mask-wearing behaviours. In particular, law enforcement officers demonstrated a strong preference for educating non-mask wearers, and indicated a reluctance to resort to civil penalties that were enacted in the state orders. Businesses expressed a preference to have government agencies enforce non-mask wearing behaviours. But there was also a widespread reluctance on the part of local law enforcement to resort to legal remedies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".