Transportation Performance Measures in Australia, Canada, Japan, and New Zealand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A trend toward greater public accountability in decisionmaking over the past decade has led many U.S. agencies to adopt performance measurement programs. The Federal Highway Administration, American Association of State Highway and Transportation Officials, and National Cooperative Highway Research Program sponsored a scanning study of how agencies in Australia, Canada, Japan, and New Zealand use performance measures in transportation planning and decisionmaking. The U.S. delegation found that transportation agencies in the countries visited use performance measures for setting priorities and making investment and management decisions to a greater extent than is typical in the United States. The team observed the most impressive application of performance management in road safety, where it was used to identify strategies to reduce fatalities. Agencies also used performance measurement to provide greater accountability and visibility to the public and elected decisionmakers. The scanning team’s recommendations for U.S. application include encouraging States to implement best practices on safety performance measurement. The team also recommends developing a data exchange and warehousing consortium for benchmarking performance among participating States, and conducting further research on performance measurement-related topics.\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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it