Bibliographic record
Abstract
Although municipalities play a significant role in the provision of police services (while others rely on provincial and federal police forces), they face constraints when it comes to police governance and accountability, including control over police budgets. The fifth report in the Who Does What series from the Institute on Municipal Finance and Governance (IMFG) and the Urban Policy Lab focuses on the role that Canadian municipalities play in policing, which functions they are best suited to perform, and how they can work better with other orders of government. Mukherjee and Kwon draw attention to the municipal policing responsibilities that arise from federal mandates or areas of federal jurisdiction. They call for an inter-governmental discussion on the division of responsibilities among federal, provincial and municipal orders of government. They suggest that municipalities should fund public safety–related functions, which should be separated from those requiring armed police. Provincial legislation on policing, they argue, should accurately reflect the delineation of responsibilities, different types of policing services, the consequent structures, and municipal obligations. Laming proposes replacing the current structure of local police services boards, which includes political representation, with a purely civilian model of governance. He provides a framework to implement this approach, ending with 10 recommendations to improve the governance and accountability of local 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".