What does robbery really cost? An exploratory study into calculating costs and ‘hidden costs’ of policing opioid-related robbery offences
Bibliographic record
Abstract
Recent attention on the opioid crisis has almost exclusively focused on this issue as a public health concern. Although we do not dispute this approach, we recognize that the opioid crisis in Canada has also generated significant policing costs—particularly in the form of robberies of pharmacies and other businesses. Much of this cost, we argue, remains unknown and/or hidden from public discussion. In this study, we present a more accurate costing of investigating robbery cases, by focusing on a series of opioid-related robberies committed by two individuals in London, Ontario. To calculate the costs, we sought to identify some of the hidden factors not commonly accounted for. Our results indicate that the cost of investigating a robbery case—from initial call to closing of the case—is comparable with previous estimates. However, as opioid-related pharmacies occur as a series of events, total costs are not insignificant. The results of this study have implications for resource allocation policies and highlight the need for a standard police costing metric and a more nuanced understanding of opioid addiction as a policing issue.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".