Correctional services during and beyond COVID-19
Bibliographic record
Abstract
Correctional services, both institutional and within the community, are impacted by COVID-19. In the current paper, we focus on the current situation and examine the tensions around how COVID-19 has introduced new challenges while also exacerbating strains on the correctional system. Here, we make recommendations that are directly aimed at how correctional systems manage COVID-19 and address the nature and structure of correctional systems that should be continued after the pandemic. In addition, we highlight and make recommendations for the needs of those who remain incarcerated in general, and for Indigenous people in particular, as well as for those who are serving their sentences in the community. Further, we make recommendations for those working in closed-custody institutions and employed to support the re-entry experiences of formerly incarcerated persons. We are at a critical juncture—where reflection and change are possible—and we put forth recommendations toward supporting those working and living in correctional services as a way forward during the pandemic and beyond.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".