Determinants of traffic safety enforcement behaviour among police officers: A narrative review
Bibliographic record
Abstract
Traffic safety enforcement (TSE), which includes policing of traffic laws, is a key component of safe transport systems. Effective TSE depends on law enforcement professionals’ (i.e., police officers’) ability and willingness to proactively engage in TSE activities. By conducting a narrative review of past literature, this article documents (i) factors reported to influence law enforcement professionals’ willingness to engage in TSE, and (ii) policy recommendations to improve TSE and thus traffic safety. The extensive literature review presented in this report identified four main types of factors that influence officers’ willingness to engage in TSE: (i) individual-level factors (e.g., officers’ perceptions of TSE), (ii) situational factors (e.g., traffic offenders’ demographic characteristics), (iii) organization-level factors (e.g., traffic citation quotas), and (iv) community-level factors (e.g., political influence). Policy recommendations to improve TSE recognized that a country’s economic condition poses significant barriers to effective TSE. Results of this literature review summarize and provide insights into factors that influence TSE and can be used by road safety professionals, including transport engineers and law enforcement professionals, to improve TSE efforts and outcomes.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".