Characteristics of the grey fleet in British Columbia
Bibliographic record
Abstract
Background: Data from around the world would suggest that attention to safety during work-related driving should be a priority as traffic accidents are the leading cause of work-related injury, death and absence from work in many countries. Purpose: This study examines the characteristics of the grey fleet (those who drive personal vehicles for the purposes of work), and the road safety programs that are presently in place in British Columbia. Method: A lGrey Fleet Employer Surveyr was distributed via email to a random sample of 15% of all employers in British Columbia Of 5023 emails delivered successfully 531 respondents were captured in the data (10.6% response), of which 104 declined participation leaving 427 who completed surveys (8.4% response rate). Results: Of those companies responding 64.4% of them reported having a grey fleet with 36.6% of employees from small companies (4-19 employees), 21.0% of the employees from medium (20-99 employees) and 12.2% of employees from large (100+ employees) companies driving personal vehicles for work-related purposes. Of those employers reporting a grey fleet 75.2% checked to make sure employees who drove had a valid driversr licence, typically at the time of hire (70.5), with 39% checking on an annual basis. Few companies (17.8%) required employees to inspect their own vehicles before starting each trip. The majority of employers (74.6%) believed it was the employeers responsibility to inspect their own private vehicles, and this was true across small (73.6%), medium (73.6%) and large (77.8%) employers. Conclusion: Employers who use grey fleets are not certain of their legal requirements under Workerrs Compensation Act (duty of care), and education and training is required concerning the employer and employee responsibilities concerning driving safety.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".