Going to the dogs? Police, donations, and K9s
Bibliographic record
Abstract
Purpose Most existing literature on K9 units has focused on the relationship between police handler and canine, or questions about use of force. The purpose of this paper is to explore the relationship between private donations to public police departments, an increasingly accepted institutional practice in the policing world, and K9 units. Specifically, the authors examine rationales for sponsoring and financially supporting K9 units in Canada and the USA. Design/methodology/approach The authors focus on four main themes that emerged in analysis of media articles, interview transcripts, and the results of freedom of information requests. Findings These four rationales or repertoires of discourse are: police dogs as heroes; dogs as crime fighters; cute K9s; and police dogs as uncontroversial donation recipients. Originality/value After drawing attention to the expanding role of police foundations in these funding endeavors, the authors reflect on what these findings mean for understanding private sponsorship of public police as well as K9 units in North America and elsewhere. The authors draw attention to the possibility of perceived and actual corruption when private, corporate monies become the main channel through which K9 and other police units are funded.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".