Policy Forum: On the Way to a Distance-Based Tax?
Bibliographic record
Abstract
This article looks at the instruments through which motorists contribute to financing the Canadian road network. Four sources of revenue are presented: fuel taxes, vehicle registration fees, carbon taxes, and tolls. A summary analysis of the revenues from these sources in Quebec and Ontario shows that they do not cover the costs of the road networks in these two provinces. In these times of technological change, revenues from fuel taxes are shrinking and those from vehicle registration fees are growing steadily and fast becoming the chief source of road financing. To redress the situation, a distance-based pricing system seems like the best solution. However, such a solution is complex to implement and could take several years to become operational. Until then, raising fuel taxes is the best way to ensure financing for road network maintenance and to facilitate the transition to a new mode of financing.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.010 |
| Insufficient payload (model declined to judge) | 0.049 | 0.006 |
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".