Embedding Safe System in Victoria: Blockers, Enablers and Improvement Roadmap
Bibliographic record
Abstract
The Safe System approach was adopted in Victoria in 2004. It has since been the cornerstone of every road safety strategy and action plan, most recently the Victoria Road Safety Strategy 2021-2030. Despite the absence of extensive and conclusive evidence, it is apparent that the limited integration of the Safe System approach into our road and transport management system has hindered its capacity to significantly reduce serious road trauma. In this project, five workshops were delivered by HA Consulting and Road Safety Victoria (RSV) to diagnose what implementation blockers exist and how road safety management systems and stakeholders could be enabled to implement the Safe System approach. Over 60 representatives attended the workshops from key road safety stakeholders and players. The anonymous workshop exercises identified a high personal alignment to the Safe System principles, a range of systemic practical day-to-day blockers, and suggestions for what could enable better Safe System aligned decision making. An improvement roadmap of concept-level projects was developed to address Safe System implementation blockers and implement the identified enabling measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".