Chinese immigrants' perceptions of the police in Toronto, Canada
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess empirically Chinese immigrants' perceptions of the police in Toronto, Canada. Design/methodology/approach Data were analyzed based on 293 surveys conducted with Chinese immigrants who participated in various community service organizations in Toronto, Canada, between March and May 2005. Ordinary least squares and ordered logit regressions are used for the analysis. Findings The paper shows that individuals who had previous contact with police rated police less favorably than those who had not had contact with police in the past. In general, people who rated police as helpful when they called them for assistance expressed a higher degree of respect for police. In addition, poor communication was a significant predictor of Chinese immigrants' perception of police prejudice. Finally, a majority of respondents expressed the concern that more bilingual police were needed in the city. Research limitations/implications As with any study utilizing a non‐probability sample, care must be taken to avoid generalizing the findings to all Chinese immigrants in Toronto. Since the sample was taken from participants of various community service organizations in Toronto, the findings may not be appropriate to generalize to the other constituencies in the Chinese community, such as young people. Practical implications The paper highlights the need for improving the quality of police services, recruiting more bilingual officers (or officers from their communities), strengthening police training in racial and cultural diversity, and reducing communication barriers to improve Chinese immigrants' evaluations of the police. Originality/value This research is the first to specifically examine Chinese communities' perceptions of law enforcement in Canada. Law enforcement can utilize these findings to improve their services and address the Chinese community's concerns; not only can this promote the police‐citizens relationship, but it can also encourage the Chinese community's participation in a crime reduction partnership.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".