7 Convergent models of police cooperation: the case of anti-organized crime and anti-terrorism activities in Canada
Bibliographic record
Abstract
In recent decades, ad hoc police structures, such as multi-jurisdictional investigative teams, have become widespread in the United States (Coldren and Sabath 1992; Jefferis et al. 1998). In their intensified fight against organized crime, police authorities have been required to improve both the flexibility of criminal investigations and the coordination of the resources devoted to the dismantling of criminal organizations. In parallel, the war against drugs, the proliferation of firearms in problematic neighbourhoods, and the emergence of particularly violent street gangs have necessitated a reconfiguration of police responses. Police agencies may thus be understood as open organizational systems; that is, systems that interact (positively and negatively) with their socio-political environment and learn expectations of and propose appropriate responses to that environment.1
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".