Bibliographic record
Abstract
This paper evaluates the traffic safety impacts of various transport pricing reforms, including fuel-tax increases, efficient road and parking pricing, distance-based insurance and registration fees, and public-transit fare reductions. This analysis indicates that such reforms can significantly reduce traffic risk, in addition to providing other important economic, social, and environmental benefits. Crash reductions depend on the type of price change, the portion of vehicle travel affected, and the quality of alternative transport options available. If implemented to the degree justified on the grounds of economic efficiency (for example, to reduce congestion, recover road and parking facility costs, and make insurance more actuarially accurate), these reforms are predicted to reduce North American traffic casualties by 40% to 60%. The low per capita traffic fatality rates in European and wealthy Asian countries result in significant part from their higher transport prices, which result in more efficient multimodal transport systems by which residents drive less and rely more on alternative modes. However, these benefits are often overlooked: pricing reform advocates seldom highlight traffic safety benefits, and traffic safety experts seldom advocate pricing reforms. Taking these steps is particularly important for developing countries now establishing pricing practices that will affect their future travel patterns and therefore crash risks.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".