More than a riot: Understanding the role of the police in crowd disturbances and moving toward a theory of police behaviour
Bibliographic record
Abstract
Civil protests. Music concerts and festivals. Sporting events. Parades and other large-scale celebrations. While these events differ in terms of their purpose, they are fundamentally all the same: they draw large crowds of people into public spaces, and, in the interest of maintaining public safety, they all require the presence of the police to monitor and manage the behaviours of individual crowd members. Even though most of these large-scale public gatherings are peaceful, crowds’ proclivity towards violence and destruction appears to be on the rise (Kaplan et al., 2020; Reid, 2020). According to the predominant theories and research on crowds and public-order policing, the manner in which the police respond to the crowd may play a role in influencing whether or not a crowd event ends peacefully (e.g., Wahlstorm, 2007). However, the inability of these theories and studies to account for discrepancies in the effectiveness of police approaches to crowd management across different events suggests there may be more to it than merely the police response that impacts the outcome of a crowd event. Using data collected from a sample of Vancouver police officers following the 2011 Stanley Cup riot, this dissertation explores some of the nuances associated with the policing side of crowd events in three separate, yet related studies. Focusing specifically on the events that transpired during the 2011 Stanley Cup riot, the first two studies explore police perceptions of the utility of the Meet-and-Greet crowd management strategy, and the potential influence the police officers themselves had on the effectiveness of this strategy during the riot. Examining police perceptions of the broader climate of policing around the time of the 2011 Stanley Cup riot, the final study explores the potential role that contextual factors play in shaping the policing of large-scale public events. By highlight some of the challenges and obstacles officers face when policing crowds, these studies may assist in deepening our understanding of public-order policing. This dissertation will outline some of practical and theoretical implications stemming from these results, as well as future directions for research focusing on the policing of large-scale public events.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".