Policing social media: Are procedural justice principles guiding Canadian police interactions online?
Bibliographic record
Abstract
Police presence on social media has become increasingly common in recent years and has arguably altered policing in many ways. Although research in this area is increasing, the growing presence of police on a range of social media platforms requires further examination of the various nuances that continue to emerge regarding this symbiosis. To that end, only a small number of studies have examined this topic from the perspective of police personnel in the Canadian context. Accordingly, drawing on in-depth interviews with police personnel overseeing police social media sites, this article examines how Canadian police services manage negativity and conflict online. The findings suggest that police services address negativity and conflict on their social media sites by drawing on the principles of procedural justice to guide their interactions. We discuss the implications of these findings and how police–public social media interactions might be improved.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.042 | 0.025 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".