State Policies and Procedures on Use of the Highway Safety Manual
Bibliographic record
Abstract
The 2010 publication of the Highway Safety Manual (HSM), 1st Edition, by the American Association of State Highway and Transportation Officials (AASHTO) was an important milestone in advancing the quality of safety analyses supporting highway investment decision making. One impediment to progress that will be addressed in this informational report is the lack of State policy on the use of the HSM in those processes. The States participating in the HSM Implementation Pooled-Fund Study (TPF-5(255)) identified the need for a compilation and synthesis of existing State policies and development of sample policy and procedures language covering a range of activities in which use of the HSM would be beneficial. The State Policies and Procedures on Use of the Highway Safety Manual Informational Report was created to present sample language as generalized statements adapted from noteworthy examples of existing language in State DOT manuals and policies. For States in which integrating the HSM into typical agency practices has been slower than desired, the informational report provides a starting point that can accelerate efforts to develop and adopt policies and procedures to support implementation of the HSM.\n
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.170 | 0.197 |
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".