The Ombudsman as a Monitor of Human Rights in Canadian Federal Corrections
Bibliographic record
Abstract
A Human Rights Approach to CorrectionsAn important challenge for many countries, including advanced democracies, is guaranteeing the human rights of its prisoners.The quality of regard to, and respect for, human rights may impact on the success of prisoners' reintegration and participation in society.A good balance between internal and external monitoring can prevent human rights breakdowns, detect violations when they occur, and rectify the situation to ensure that they do not happen again.Striking the appropriate balance between internal and external monitoring is not easy.Canada, like many other countries, has struggled with establishing and maintaining this balance.Even so, accountability and transparency in decision-making remains a fundamental challenge of a compliant human rights monitoring system.The best approach to ensure that the rule of law is upheld in corrections is to conceptualize the business of corrections as a human rights business. 1When government has exceptional authority over its citizens, the potential for abuse of powers is great and the protections of fundamental rights must be a core preoccupation of those empowered and trusted with such exceptional powers.In a correctional context, every aspect of a prisoner's life is heavily regulated by correctional authorities.Correctional authorities make thousands of decisions every *
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".