Gender differences in understanding police perspectives on crowd disorder
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the nature and dynamics of crowd disorder from the perspective of the police in a Canadian context, as well as to extend this perspective to include the opinions of female police officers. Design/methodology/approach A total of 460 Vancouver police officers participated in this study. Following the 2011 Stanley Cup riot, police officers received mail-based questionnaires focussed on gathering information concerning police perceptions of the crowd and the police response in riot situations. A total of 15 response items were analysed using descriptive approaches and confirmatory factor analyses. Findings The study findings revealed that, in addition to being multidimensional, the police perspective of crowd disorder may be contingent upon certain officer characteristics. Although, the police perspective can generally be categorized by four overarching constructs: dichotomous crowd, homogeneous threat, strict policing and tactical response; it becomes more complex once the officers’ gender is taken into consideration. The results suggest that the male and female police officers may have some differing views about the nature of crowds and the type of police response required to manage disorderly crowd situations. Originality/value In addition to being the first study to analyse police perceptions of crowd disorder in a Canadian context, this research is the first to include the points of view of female officers.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".