Transportation Research Current Practice and a Look Forward CHRISTOPHER HEDGES, Transportation Association of Canada
Bibliographic record
Abstract
The waning years of the 20th century have seen major changes in the way we plan, coordinate, and conduct transportation research, primarily as a result of numerous trends in the transportation sector and in society as a whole. There has long been widespread recognition that transportation is the foundation of our economy and our quality of life. More recently, however, transportation agencies have begun to see their role as much more than simply providing infrastructure. The mission statements of today’s transportation agencies typically include enabling the movement of people and goods in an efficient, convenient, safe, and environmentally sustainable manner. In their new roles, transportation providers must interact with—and, at times, compete with—other government departments and quasi-governmental agencies. Transportation agencies have become more focused on making sound investments in transportation solutions that address strategic issues and needs. This change requires an increased emphasis on the careful allocation of funds to achieve the maximum benefits and outcomes of our research programs. It also necessitates that transportation research be expanded beyond traditional infrastructure concerns to include areas such as policy, economics,
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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 | 0.042 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.024 | 0.027 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".