Who’s Policing the Crowd? A Typology of Officers Who Policed the 2011 Stanley Cup Riot
Bibliographic record
Abstract
Abstract Moving away from high-profile, hard-lined tactics, approaches to crowd policing have become increasingly geared towards softer, negotiation-based policing methods. Despite their perceived benefits, there exist a number of challenges relating to the successful implementation of these low-profile approaches, most notably being the officers themselves. Given the central role officers play in responding to and managing crowd situations, it is important to know what types of officers are actually deployed to police these events. Utilizing survey data collected in the aftermath of the 2011 Stanley Cup riot, this study employed a cluster analysis to examine the similarities and differences amongst the officers who were deployed to police this event. This technique produced two distinct clusters, which were subsequently used to examine the relationship between officer characteristics and orientations towards crowd policing. By providing a possible explanation for the success and/or failure of a particular crowd management strategy, these results may help police departments in their preparations for future crowd events.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".