Mechanisms for Eliciting Cooperation in Counterterrorism Policing: Evidence from the United Kingdom
Bibliographic record
Abstract
This study examines the effects of counterterrorism policing tactics on public cooperation among Muslim communities in London, U.K. The study reports results of a random‐sample survey of 300 closed and fixed response telephone interviews conducted in Greater London's Muslim community in February and March 2010. It tests predictors of cooperation with police acting against terrorism. Specifically, the study provides a quantitative analysis of how perceptions of police efficacy, greater terrorism threat, and the perceived fairness of policing tactics (“procedural justice”) predict the willingness to cooperate voluntarily in law enforcement efforts against terrorism. Cooperation is defined to have two elements: a willingness to work with the police in anti‐terror efforts, and the willingness to alert police upon becoming aware of a terror‐related risk in a community. We find that among British Muslims, both measures of cooperation are better predicted by procedural justice concerns than by perceptions of police efficacy or judgments about the severity of the terrorism threat. Unlike previous studies of policing in the United States, however, we find no correlation between cooperation and judgments about the legitimacy of police; rather, procedural justice judgments influence cooperation directly.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".