Allocution présidentielle, 44e Congrès annuel de la Société Canadienne de Science Économique (SCSE), Québec, le 5 mai 2004 : où en sommes-nous avec les coûts sociaux du transport?
Bibliographic record
Abstract
Le secteur des transports se caractérise notamment par des écarts significatifs entre les coûts privés et les coûts sociaux. Ces écarts entraînent des distorsions qui sont source d’inefficacités économiques pouvant, en principe, se corriger à l’aide des outils économiques traditionnels que sont la tarification et la taxation. Nous examinons les raisons pouvant expliquer pourquoi on observe très peu de cas de taxation ou de tarification dans le secteur des transports. Nous discutons également de l’efficacité de ces outils et de leur pertinence dans un contexte où l’utilisation de ces outils relève des décideurs publics. Enfin, nous discutons d’avenues alternatives et complémentaires à la tarification et la taxation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".