Mediated public perceptions of police encampment clearances in Canada and France: a cross-national study
Bibliographic record
Abstract
This qualitative study investigates public perceptions of police encampment clearances in Canada and France. Our focus is on how YouTube users in these two countries judge police conduct in response to videos of police clearing encampments. Research on police public perceptions and police legitimacy typically focuses on public views of the police institution and perceptions stemming from direct interactions with officers. An analysis of YouTube comments on police intervention videos offers important insights into a relatively unexplored intermediate case: the mediated judgements on concrete, specific police action from individuals outside of the perceived police intervention. Further, the scholarship specifically examining social media and public perceptions of policing in cross-national contexts is almost nonexistent. To address these gaps, we ask the following: What do comments on YouTube in response to videos of police encampment clearances reveal about police legitimacy in Canada and France as perceived by social media users? A total of 8,091 user-generated comments across 25 Canadian YouTube videos and 7,086 comments across 29 French YouTube videos were collected, sorted, and examined using qualitative media analysis. Our analysis confirms that the issue of police legitimacy generates dissensus and polarisation. It demonstrates that public perceptions of the legitimacy of the police clearance are strongly tied to the legitimacy of the encampment itself, and more broadly to moral judgements about the groups targeted by the police. Our findings also reveal that French police are more often perceived as violent than their Canadian counterparts.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".