‘No one wants to end up on YouTube’: sousveillance and ‘cop-baiting’ in Canadian policing
Bibliographic record
Abstract
Citizen recordings of police-public encounters are increasingly surfacing on social media, especially those in which individuals intentionally create confrontational situations to provoke a desired response from police officers. The latter is a form of, what we term, cop-baiting, driven mainly by the ubiquitous sousveillance of police by citizens. Although the literature has explored how media can impact public perceptions of police and police legitimacy, little research has examined cop-baiting social media content specifically or the impacts of cop-baiting forms of sousveillance. The current study investigates police officers’ perspectives, concerns, and experiences of these phenomena while concurrently exploring the perceived consequences of these on officers and policing, representing a novel departure from previous work. To examine police sousveillance and cop-baiting, we draw on qualitative interviews with over sixty police officers from across Canada who have been involved in the policing of politically contentious events. Most notable among the findings were that officers reported a range of impacts of sousveillance and cop-baiting, including occupational stress, effects on families and loved ones, and professional and reputational implications. It was also uncovered that police sousveillance and cop-baiting could significantly undermine police legitimacy and public trust. The current study concludes with some final thoughts on the meaning of cop-baiting and the problematic nature of this activity, a future research agenda, and considerations for police and policymakers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".