Bibliographic record
Abstract
A variety of road safety initiatives have been introduced throughout North America over the past 10 to 15 years that are aimed at improving the level of safety on highways and in communities. These initiatives have achieved substantial reductions in the frequency and severity of crashes and have acquired a broad range of specialist road safety knowledge and skills. Despite such successes, in reviewing the extent to which most jurisdictions have achieved their objectives of establishing a high quality and sustainable road safety system throughout, there remain two significant system deficiencies: (1) the ad hoc nature of many road safety initiatives, in terms of the short term duration of applying these initiatives and the lack of integration between them; and (2) the low level of importance, and consequently limited resources, allocated to road safety, and the limited participation of local communities in promoting road safety. The next generation of road safety initiatives must respond to these issues through the development and implementation of Community Road Safety Plans. These can be described through the consideration of what has been termed the “6 C’s” of Community Road Safety Planning: (1) comprehensive scope; (2) coordinated initiatives; (3) cooperation and contribution of stakeholders; (4) consultation within communities; (5) competence of officials; and (6) commitment of community. This paper provides an overview of the foundation program in the United Kingdom, the Gloucester Safer City project and the planning and current implementation of similar programs in British Columbia and Alberta.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.007 |
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".