Bibliographic record
Abstract
Over the last 10 years there has been a growing trend among automobile insurance companies to become involved in road safety engineering programs. While the involvement of insurance companies in driver education and vehicle design initiatives is common, insurance company initiatives aimed at the engineering element of road safety is a relatively new trend. This research summarizes the major road safety engineering programs undertaken by six insurance companies in Australia, Canada, and the United States, and presents some of the results achieved. The research finds that the immediacy of the benefit derived from road safety engineering improvements, coupled with an expanding knowledge base in this field, are contributing to the growth in interest in road safety among insurance companies. The financial interest of insurance companies in reducing crash frequencies and severities, as well as any related positive public image that road safety advocacy can generate, will likely mean that more insurance companies will be exploring avenues for participation in road safety programs. Opportunities exist for cooperation between the insurance industry and transportation engineers, and they should be pursued for mutual benefit. Although the ultimate responsibility and authority for roads should remain with public agencies, the incentive and emphasis that insurance companies place on road safety provide a unique opportunity to help reduce the daily risks that we face in a mobile world.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 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".