Royal Commissions and Canada's Transport Policy Community: The Changing Dynamics of Political Innovation
Bibliographic record
Abstract
Introduction: Placing Royal Commissions in Context 11°Yal C one ommissions have been a mainstay of Canadian transportation policy making.bod e In each generation since the First World War, government has charged such a eff ,Y to develop a new transportation strategy.This paper takes stock of the latest such the "Royal Commission on National Passenger Transportation" chaired by Louis e',/ a7ullian• The potential influence of the Hyndman commission's report will be mated in a comparative context that spans both time and space.By connecting the inY to li ian Commission's efforts with the work of its predecessors, one gains insight t Lue domestic political forces that will serve to either constrain or facilitate its ' ence on reinventing government's role in Canadian transportation.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.031 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".