Demystifying confidence in different levels of the police: evidence from Shanghai, China
Bibliographic record
Abstract
Extending Fei's ‘differential mode of association’ (1992) and Cao et al.'s argument that Chinese trust is layered and hierarchical (2015), this study explores the differential confidence in the different levels of police agencies and brings various forms of trust into the study of confidence in the police. Results from a household random sample reveal that Shanghainese make a distinction between hierarchical levels of the police. Their confidence level toward their municipality police department is similar to that toward the Ministry of Public Security while their confidence levels toward the police at stations and Paichusuo are more alike. In addition, the multi-variate regression analyses indicate that institutional trust is the dominant factor for explaining confidence in both local and upper-level police. Media trust, sense of safety, financial satisfaction and collectivism are significant predictors in both models. Obeying the law, gender and class influence confidence in the local police but not the upper-level police while intermediate trust and education have a significant effect only on confidence in the upper-level police. It is concluded that assessment of local police is central to the understanding of public confidence in China.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".