When crises collide—Policing a pandemic during social unrest
Bibliographic record
Abstract
In 2020, the United States was shaken by concurrent crises: the COVID-19 pandemic and protests for racial equality. Both crises present significant challenges for law enforcement. On the one hand, the protests for racial equality drew the public’s attention to the criminal justice system’s disparate treatment of Blacks and other people of colour. On the other hand, the pandemic required the expansion of police duties to enforce public health mandates. To ensure compliance, law enforcement may arrest, detain, and even use force to prevent the transmission of communicable diseases that may have an irreversible impact on human health, such as COVID-19. Policing, however, is at a critical point in America. The government is expanding police powers for the sake of public health; all the while, public indignation about police (ab)uses of power has fuelled calls for its defunding. It is therefore important to explore Americans’ views of policing pandemics during periods of social unrest, focusing on the recognition that socio-economic and racial inequities shape perceptions. The data from this project derives from surveys with Americans on the specific topics of race, policing, racial protests, and COVID-19. The study finds that Americans perceive the police as legitimate overall; however, there are divergences based on race, gender, and marital status. These differences may contribute meaningful insights to the current discourse on police legitimacy in America.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".