Police Innovations, ‘Secret Squirrels’ and Accountability: Empirically Studying Intelligence-led Policing in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an environment of fiscal constraint and growing fear of catastrophic events, police services are turning to intelligence and analytic technologies to conduct aggressive information gathering and risk analysis. The present study uses 86 in-depth interviews and participant observation to explore the integration and utilization of intelligence-led policing (ILP) in a Canadian context. From this analysis, we identify how police cultures, organizational context and situational pace of policing constrain an intelligence-led framework. Further, we illustrate how police services have rhetorically adopted ILP and translated it to mean accountability in a time of austerity. By translating ILP, Canadian police services have been able to redefine success within their services without necessarily attending to the outcomes of their practices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it