Bibliographic record
Abstract
The spate of youth road fatalities that we have seen in New South Wales in the last quarter of 2006 represents tragedies that no community should have to bear. These are made all the more tragic to those of us who know how these kinds of events can be prevented. There are evidence based solutions. In Australia we have influenced significant change in community and political attitudes in favour of road safety in recent times. But somehow, we as a road safety “profession” have not entirely convinced the global – or even the Australian community – that it is best to choose safety intervention over “personal freedom” or other socioeconomic benefits. Within the Australasian College of Road Safety, we have debated to what degree we should be a community advocate versus a professional support organisation. For a while, many of us took the conservative view that we should work towards a strengthening of our members' skills and knowledge before we embark on public advocacy. This has been a sensible approach. But increasingly, we are finding a role in ‘ advising’ community leaders on some key issues. We have established a series of policy positions on major road safety issues based on our collective knowledge base. Beyond this we have organised seminars and forums for public discussion as well as responded to questions by media organisations.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.024 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".