Blame attribution analysis of police motor vehicle collision reports involving child bicyclists
Bibliographic record
Abstract
BACKGROUND: Injuries resulting from collisions between a bicyclist and driver are preventable and have high economic, personal and societal costs. Studying the language choices used by police officers to describe factors responsible for child bicyclist-motor vehicle collisions may help shift prevention efforts away from vulnerable road users to motorists and the environment. The overall aim was to investigate how police officers attribute blame in child (≤18 years) bicycle-motor vehicle collision scenarios. METHODS: A document analysis approach was used to analyse Alberta Transportation police collision reports from Calgary and Edmonton (2016-2017). Collision reports were categorised by the research team according to perceived blame (child, driver, both, neither, unsure). Content analysis was then used to examine police officer language choices. A narrative thematic analysis of the individual, behavioural, structural and environmental factors leading to collision blame was then conducted. RESULTS: Of 171 police collision reports included, child bicyclists were perceived to be at fault in 78 reports (45.6%) and adult drivers were perceived at fault in 85 reports (49.7%). Child bicyclists were portrayed through language choices as being irresponsible and irrational, leading to interactions with drivers and collisions. Risk perception issues were also mentioned frequently in relation to poor decisions made by child bicyclists. Most police officer reports discussed road user behaviours, and children were frequently blamed for collisions. CONCLUSIONS: This work provides an opportunity to re-examine perceptions of factors related to motor vehicle and child bicyclist collisions with a view to prevention.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".