Exploring police-reported cybercrime in Canada
Bibliographic record
Abstract
Purpose Police-reported incidents of cybercrime appear to vary dramatically across Canadian municipal police services. This paper explores cybercrime reporting by police services in eight of Canada's largest municipalities, assessing (1) variation over time; (2) variation across jurisdictions; and (3) correlates of reporting volumes. Design/methodology/approach Data was collected from a combination of national Uniform Crime Report statistics and annual reports by police services. Two repeated one-way ANOVA tests and a Pearson's r correlation matrix were used to assess variation and correlation. Findings Findings suggest that police-reported cybercrime varies significantly across jurisdictions but not over time. Moreover, negative relationships were observed between police-reported cybercrime incidents per 100,000 residents and calls for service per 100,000, as well as number of sworn officers per 100,000. Research limitations/implications The study assessed a small sample of cities ( N = 8) providing 32 data points, which inhibited robust multivariate analyses. Data also strictly represents calls to police services, therefore excluding alternative resolutions such as public–private interventions. Practical implications Canadian provincial and federal governments should consider engaging in high-level talks to harmonize cybercrime reporting strategies within frontline policing. This will mitigate disparity and provide more accurate representations of cybercrime for future policy development. Additionally, services should revisit internal policies and procedures, as it appears that cybercrime is deprioritized in high call volume situations. Originality/value This paper introduces previously unreported data about police-reported cybercrime incidents in Canada. Furthermore, it adds quantitative evidence to support previous qualitative studies on police responses to cybercrime.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".