Expanding the Boundaries of Research Involving Death and Near Death When Liberty is Attenuated
Bibliographic record
Abstract
The notion that the state has special responsibilities to protect and care for persons who are lawfully detained is well established in international and domestic law. When government reduces a citizen's liberty so egregiously, the quid pro quo must be to ensure that the inmate is kept in an environment which strives to reduce the risks of disease, mental health problems, self-harm and violence, as well as providing rehabilitative and therapeutic supports. This ideal is not always attained and, at its worst, the death of an inmate may result. Howard Sapers is no doubt correct in his companion article in which he has highlighted the need for the development of a Canadian Forum for Preventing Deaths in Custody. In this comment, it is argued that the research and policy mandate of such a new entity should be broadened to include additional types of institutions, near-death or similarly serious calamities, early post-release events and former inmates and others subject to state supervision while living in the community. A more expansive approach will produce better outcomes in a wider range of institutions and community settings for citizens who are far more vulnerable than those who live without such state controls.
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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 | 0.250 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.117 |
| Scholarly communication | 0.021 | 0.048 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".