Funnelling through foundations and crime stoppers: how public police create and span inter-organisational boundaries
Bibliographic record
Abstract
Public police require a reliable supply of resources to operate effectively, and police increasingly seek resources from private organisations and individuals. Since police departments are public bodies, they encounter boundaries in doing so. The key challenge for public police is how to access private resources for initiatives while seeming to avoid real or alleged influence from private entities providing them. This article examines policing across inter-organisational boundaries and boundary negotiation by investigating two kinds of private organisations – police foundations and Crime Stoppers organisations – operating in Canadian jurisdictions, and which reflect significant trends in public police practices. Both organisational models were established by public police in the United States in 1970s, have proliferated, and now commonly operate adjacent to – but not within – North American police departments. Both models, and especially how they connect, create distance from, and otherwise relate to public police, lend insight into how boundaries are maintained, negotiated, and spanned. Implications of these arrangements for future research and the public good are discussed.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.020 | 0.038 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".