Police pursuits in Queensland: research, review and reform
Bibliographic record
Abstract
Purpose Police high‐speed pursuits present a difficult area for police managers and policy makers because of the important need to balance public safety with the mandate to enforce laws. The issue of police pursuits has been relatively under‐researched in Australia. The overall purpose of the paper is to provide a descriptive analysis of the characteristics surrounding police pursuits in Queensland, Australia. Design/methodology/approach Considers recent events involving high speed pursuit‐related fatal accidents and research into police pursuits which has illuminated clearly the significant risks for both community and police organisations associated with pursuits. Uses data collected in Queensland over a five‐year period. Findings The results show that approximately 630 pursuits occur per year in Queensland across the study period, and that half of all pursuits are initiated for traffic offences while an additional quarter are initiated for stolen cars. A total of 29 per cent of pursuits involved a collision, 11 per cent resulted in some sort of injury, and 11 people were killed during the five‐year study period. In relation to an issue that appears to justify the initiation of some police pursuits – that fleeing drivers provide opportunities for police to apprehend serious offenders – examination of the charges data against the fleeing driver showed that very few apprehended drivers were charged with crimes more serious than what was known at the time the pursuit was initiated. Originality/value The findings in this study illuminate the importance of adopting more restrictive police pursuit policies.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.028 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".