COVID-19 in the North American Prison System and the Public Health Response to the Epidemic
Bibliographic record
Abstract
With a sharp increase in the number of the 2019 coronavirus disease (COVID-19) cases worldwide, one of the hardest hit institutions are high-density prison systems. Incarcerated individuals are at a disproportionate disadvantage of contracting COVID-19 due to their previous medical history of underlying conditions, the densely packed quarters they reside in, as well as increased contact with correctional staff who frequently go in and out of prisons. This calls for public health efforts to ensure that there are guidelines in place in order to manage COVID-19 in the prison systems in a structured manner, and to reduce mortality related to the disease among prisoners. The current public health response has been to follow recommendations from the Centers for Disease Control and Prevention, as well as push towards decarceration of those individuals who are least likely to re-offend. Finally, with continued vaccination rollouts, researchers encourage priority vaccination of both prison staff and prisoners in order to control the COVID-19 outbreaks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".