Under the Influence? Factors That Impact Canadian’s Confidence in Police
Bibliographic record
Abstract
The public’s confidence in police is a crucial factor to a police department’s ability to serve its community effectively. However, not everyone in a democratic society feels confident in the police’s ability to protect and serve their community. Factors including race, gender, age, education, income, neighbourhood crime perceptions, and past discrimination have all been found to have significant impacts on an individual’s confidence in police. These factors have not been evaluated in tandem, nor have they been adequately reviewed in a Canadian context. Using the 2014 Canadian General Social Survey, this study answers the following three questions: 1) How does confidence in police vary by education? 2) Do neighbourhood perceptions of crime and household income affect confidence in police? Using Kimberlé Crenshaw’s theory of intersectionality to identify interacting and multiplying dimensions of disadvantage, this paper further explore 3) how does confidence in police differ across visible minorities and immigrants with similar levels of education? Through descriptive statistics, binary and ordered logistic regressions, this study found that, overall, education was positively associated with confidence in police; individuals who believe they live in high crime neighbourhoods were less likely to have confidence in the police; and household income negatively impacts confidence in police for those who make under $79,999 and visible minorities. Lastly, this study found that immigrants with higher education were less likely to be confident in the police, a finding revealed through an intersectional analysis relative to white, native-born individuals. Police services and policymakers may find these results useful to improve community perceptions and relationships with the public.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".