Bibliographic record
Abstract
Plural Policing in Comparative Perspective: Four Models of Regulation In this article the main findings and conclusions are presented of an international comparative study on the pluralization of policing in five countries (England and Wales, Canada, Belgium, Austria, and the Netherlands). We focus on the question: what are the main differences and similarities in plural policing between these countries, and how can these be understood? In answering this question much attention is given to the position of non-police providers of policing (employed by municipalities or security companies) in relation to the regular police. To understand the peculiarities of this pluralization we paid attention to legal, historical, cultural and political aspects, to the organization of the regular police and the position of private security. This study shows that the pluralization of policing has not been the result of some goal-intended governmental policy, but more an incremental process or the effect of an accumulation of unintended consequences. In the last section we present four models of regulation of plural policing that may be relevant to imagining potential future policy developments.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".