This article has been peer reviewed.
Bibliographic record
Abstract
Background: People in custody are more likely to die prematurely, especially of vi-olent causes, than similar people not in custody. Some of these deaths may be preventable. In this study we examined causes of death (violent and natural) among people in custody in Ontario. We also compared the causes of deaths in 3 custodial systems (federal penitentiaries, provincial prisons and police cells). Methods: We examined all available files of coroners ’ inquests into the deaths of people in custody in federal penitentiaries, provincial prisons and police cells in Ontario from 1990 to 1999. Data collected included age, cause of death, place of death, history of psychiatric illness and history of substance abuse. Causes of death were categorized as violent (accidental poisoning, suicide or homicide) or natural (cancer, cardiovascular disease or “other”). Crude death rates were esti-mated for male inmate populations in federal and provincial institutions. There were inadequate numbers for women and inadequate denominator estimates for police cells.
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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.464 | 0.315 |
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".