Bibliographic record
Abstract
The subject of this article is police reform in North America. The article is divided in two parts. The first part reviews the recent past with respect to reforming the police in Canada. I examine the case of the reform of the Montreal police, which tried to implement an approach modelled on COP-POP principles. The reform process lasted for some 18 years and covers two periods. From 1987 to 1993, the service tried unsuccessfully to reform the mentalities of its officers, without changing the structures of the force. From 1994 until today, it introduced significant changes in its structures but was forced after 2002 to reestablish many of the structures it had abolished.The reform suffered from a split of leadership between police and civilians and produced a divorce between the uniformed patrol and plainclothes criminal investigation. In the second part of the paper, I assess the impact on the police reform movement of the moral panic generated by the 9/11 terrorist attacks. I argue that very deep budgetary cuts in the US, a new reliance on physical coercion and outsourcing to the private sector may bring the COP-POP reform movement to a standstill. I conclude that current developments in policing are deepening the gap between security and justice in policing.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".