Citizen Characteristics, Neighbourhood Conditions, and Prior Contacts with the Police: A Comparative Study of Public Satisfaction with the Police
Bibliographic record
Abstract
This study takes a comparative approach to examine public satisfaction with the police, focusing on three theoretical models: the demographic model, the neighbourhood conditions model, and the prior contacts with the police model. Using survey data collected from two mid-sized communities in the U.S. and Canada, this study analyzes the similarities and differences in the factors affecting satisfaction with the police with both statistical methods and random forests analysis. The statistical results suggest a great amount of similarity in the effects of theoretically relevant factors across the two samples. The random forests analysis further points to the consistent importance of age, quality of life, and education in predicting public satisfaction. In addition, both analyses find that the effects of age and quality of life are stronger for the sample in the U.S. than those for the sample in Canada. This study suggests that police departments in these jurisdictions could effectively improve satisfaction with the police by addressing quality of life issues in their communities and improving their relationship with younger citizens and citizens with lower levels of education through better interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".