Drunken goons, criminal idiots, and mayhem: Toward a police perspective on disorderly and riotous crowds
Bibliographic record
Abstract
Crowd disorder, which refers to a wide range of both non-violent and violent public gatherings, involves a complex interaction between the crowd and the police (Stott & Reicher, 1998a).Given that the police are on the frontlines dealing directly with the crowd, they are in the unique position to understand the initiation and development of these crowd situations.Thus, their insights have the potential to offer valuable information for dealing with, and even preventing crowd disturbances.Despite an increased interest in understanding the police perspective, however, the vast majority of research has focused on the behaviour and perceptions of the crowd (Stott, 2003;Drury, Stott & Farsides, 2003).Further, the studies that have concentrated on the police viewpoint have been narrow in scope and predominantly conducted in Britain (e.g., Stott & Reicher, 1998a;Drury, Stott & Farsides, 2003).In an attempt to extend this literature, this thesis aims to develop the police perspective in a new context.Specifically, based on the events of the recent 2011 Stanley Cup riot, a sample of 460 Vancouver police officers was surveyed concerning their perceptions of the crowd and methods of crowd management.The results reveal that the while the police perspective can be reduced to four distinct factors, it becomes more complicated when the officers' characteristics are introduced into the equation.The implications of these findings for both police departments and future researchers will be discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.030 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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 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".