COVID-19 in the North American Prison System and the Public Health Response to the Epidemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it