COVID-19, Human Rights and Public Health in Prisons: A Case Study of Nova Scotia’s Experience During the First Wave of the Pandemic
Bibliographic record
Abstract
The importance of preventing outbreaks in prisons during a pandemic, such as COVID-19, cannot be overstated. The risk of the infection spreading rapidly once inside these institutions is much higher than in the community, due to the underlying vulnerabilities of prison populations and the congregated living nature of prisons. This article documents the Nova Scotia provincial prison system’s experience in dealing with COVID-19 during the first wave, including its uniquely swift decarceration efforts. One goal of this investigation is to identify a set of best practices that can help Canadian prisons systems with their short-term responses to crisis in a manner that is compliant with both international and national public health policies and human rights. Another goal of this investigation, based on the systemic weaknesses highlighted by the pandemic, is to advance longerterm recommendations that would improve the criminal justice system and help maintain lower levels of incarceration. On ne saurait trop insister sur l’importance de la prévention des épidémies dans les prisons lors d’une pandémie telle que celle de la COVID-19. Le risque que l’infection se propage rapidement une fois à l’intérieur de ces institutions est beaucoup plus élevé que dans la collectivité, en raison des vulnérabilités sousjacentes des populations carcérales et de la nature de la vie en prison. Dans cet article, nous faisons état de l’expérience vécue dans le système pénitentiaire provincial de la Nouvelle-Écosse face à la COVID-19 lors de la première vague, y compris les efforts déployés en vue d’une mise en liberté plus rapide. L’un des objectifs de cette enquête est d’identifier un ensemble de bonnes pratiques qui peuvent aider les systèmes pénitentiaires canadiens à réagir à court terme à la crise d’une manière qui soit conforme aux politiques de santé publique nationales et internationales ainsi qu’au respect des droits de la personne. Un autre objectif de cette enquête, basé sur les faiblesses systémiques mises en évidence par la pandémie, est de proposer des recommandations à plus long terme qui amélioreraient le système de justice pénale et contribueraient à maintenir des niveaux d’incarcération plus bas.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".