Bibliographic record
Abstract
From a health perspective, prisoners repre-sent a special segment of the population.First, their backgrounds often include family dysfunction, low socioeconomic status, substance abuse, little education and an inordi-nate rate of mental health problems. Second, the prison is a totally controlled environment with its own culture that includes violence, coercion and drug use, and its own security tools such as classification, discipline and segregation. In a prison environment, security always trumps other concerns. As a result, the vulnerable can easily become more vulnerable, and the healthy can soon become unhealthy. Much public attention has been paid recently to Bill C-10,1 the omnibus federal crime bill that will affect sentencing, parole, corrections and pardons. Without substantial expansion of the prison system, this legislation combined with other recent criminal law will increase prison populations, exacerbating the current overcrowd-ing in most institutions. This article provides an overview of the current health status of the Cana-dian prison population, recent legislation and the impact of overcrowding, with a predominant focus on the federal system. Canada has 2 correctional systems. At the federal level, all prisoners serving sentences of 2 years or more are confined in penitentiaries operated by the Correctional Service of Canada. As of 2010, there were 13 531 prisoners in 57 federal institutions.2 In provinces and territories, prisons and jails house prisoners serving sen-tences of less than 2 years and people detained on remand pending trial. Each jurisdiction has its own statutory framework governing its respec-tive correctional system. Health care in corrections The Corrections and Conditional Release Act stipulates that all federal prisoners shall receive “essential health care ” in conformity with “pro-fessionally accepted standards.”3 Conventions rat-ified by Canada, such as the International
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.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.462 | 0.162 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".