Bibliographic record
Abstract
This synthesis report provides a review of the state of the practice of road safety audit (RSA) and road safety audit review (RSAR) applications for U.S. states and Canadian provinces. Transportation safety professionals with these agencies and with local and regional entities, as well as others in both the public and private sectors, may be interested in this documentation of international, state, and some local agency approaches to the use of these tools in comprehensive safety programs. This synthesis places emphasis on North American applications. However, this document also discusses international practice as RSAs were first introduced in the United Kingdom more than 20 years ago, and RSAs have been extensively applied in New Zealand and Australia since the 1990s. This document promotes the use of RSAs and RSARs. The increased use of these applications may help reduce roadway crashes and fatalities. For this synthesis report survey responses were received from 38 state departments of transportation (DOTs) and 6 Canadian provinces. The state of the practice was developed based on this 2003 survey, state and local agency practices, Federal Highway Administration and National Highway Institute sponsored training for state DOTs, local agency training experiences, international practices, a literature review, and personal contacts.
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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".