Bibliographic record
Abstract
This article assesses the major recommendations made in the Ontario’s Independent Police Oversight Review by Justice Tulloch of the Ontario Court of Appeal with an emphasis on how they fit into a broader system of police review, governance, and improvement. The first part will examine the role of criminal investigations of deaths and serious personal injuries caused by the police. The second will examine the role of independent complaints process and the third will examine the adjudication of complaints against the police that result in disciplinary charges. The concluding part will examine various ways to complete the independent review system including the role of coroners’ inquests, those who govern the police, a proposed Policing College that may move the police towards a regulated profession model and finally our main proposal to supplement the reforms recommended by Justice Tulloch: the creation of community advisory board(s) designed to build better relationships between those who review the police and the communities most affected by policing. This article examines the limits of formal law reform, especially reforms designed to attain the illusive quality of public confidence and examines the importance of politics, history and sociology.
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 | 0.255 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".