PRELIMINERY DRAFT: NO QUOTATIONS WITHOUT PERMISSION PLEASE.
Bibliographic record
Abstract
The transportation sector is the biggest single source of Greenhouse Gas (GHG) emissions in Canada. Federal and Provincial efforts to reduce GHG's in this sector have been underway in earnest since the creation (in 1998) of the "Transportation Table", one of 15 sectoral groups set up to develop emissions reducing strategies and legislation. Now, as policy measures and new vehicle technologies begin to emerge it might appear as though we are finally on our way towards honouring our Kyoto commitments. However current and future efforts in the public and private sectors may fall well short of their goals. Despite the best of intentions, a complex web of incentive compatibility problems at both individual and institutional levels threaten the success of attempts to reduce GHG's in transportation. We outline these incentive compatibility problems and assess their implications, with particular reference to passenger transportation. We argue that the process of obtaining reductions in GHG emissions is best served by an approach that provides deliberate policy actions that allow us to learn where the real gains are to be made. Given our current lack of accurate information about the positive and negative synergies created by bundles of policy measures, the best way forward may well be to hasten slowly
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.502 | 0.400 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".