Research Study into the Speed Behaviour of Long and Short Haul Heavy Vehicle Drivers
Bibliographic record
Abstract
In 2005, the Roads and Traffic Authority (RTA) commissioned AMR Interactive to conduct a speed knowledge, attitudes and self reported behaviour research study to identify the reasons why long and short haul heavy vehicle drivers’ speed, evaluate the role of enforcement and the types of measures that would influence the drivers to keep within the speed limits. The qualitative stage included 10 face to face interviews and the quantitative stage included a telephone survey of 376 heavy vehicle drivers. The highest risk groups identified were younger short haul, younger long haul and older long haul heavy vehicle drivers. About one in ten drivers reported having been booked for speeding in the last 12 months and similar proportions reported that they would be willing to drive more than 10 km/h over the limit while 15% stated they failed to stay within the speed limit in built up areas. About a quarter of drivers reported experiencing some pressure to speed to meet deadlines. Drivers reported that on-road police enforcement would have the greatest impact on their attitudes and behaviour. Possible countermeasure strategies include development of an education strategy addressing attitudes to speeding, situational triggers, planning trips and rest breaks, encouraging companies to develop and implement anti-speeding policies and increasing visible, unavoidable police enforcement.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".