Developing core national indicators of public attitudes towards the police in Canada
Bibliographic record
Abstract
Police departments regularly conduct public opinion surveys to measure attitudes towards the police. The results of these surveys can be used to shape and evaluate policing policy and practice. Yet the extant evidence base is hampered when people use different methods and when there is no common data standard. In this paper we present a set of 13 core national indicators that can be used by police services across Canada to ensure measurement quality and draw proper comparisons between regions and over time. Having identified a set of 50 survey questions through an expert consultation process, we field those items on a quota sample of 2527 Canadians. Our analysis of the survey data has three stages. First, we use confirmatory factor analysis to assess scale properties. Second, we use substitution analysis to identify 13 single indicators that ‘best stand in’ for each scale. Third, we use the set of 50 and the sub-set of 13 measures to test procedural justice theory for the first time in the Canadian context. Overall, those commissioning and managing public attitudes surveys can use the 13 core indicators as a conceptually-rich and empirically-validated tool through which to understand local survey data in the context of other municipal, provincial, territorial and national contexts.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".