Working Like a Dog: A Mixed-Method Study of Public Support for Police Dogs and Their Utilities
Bibliographic record
Abstract
Working dogs play integral roles across many human workplaces. This is no exception in the criminal justice system, and policing more specifically, where police dogs are used in various capacities. Many questions remain, however, regarding the public’s perceptions of dogs in different working contexts. Drawing upon data from a sample of Canadian and American adults (n = 201) obtained via Amazon’s Mechanical Turk, the present research explores public perceptions of working dogs’ utilities, with an emphasis on police dogs. The findings reveal that while participants overwhelmingly supported working dogs in health and wellbeing contexts, they expressed more mixed perceptions regarding police dogs. The findings also reveal that police dogs’ utilities are related to participants’ overall support for police dogs, but that the specific relationship varies as a function of the utility. Amidst growing concerns regarding the use of police dogs, these findings may help police organizations incorporate evidence-based decision-making related to the deployment of police dogs moving forward.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".