A White Paper on Reforming Canada’s Transportation Policies for the 21st Century
Bibliographic record
Abstract
While much of the developed world struggles with debt and chronically low growth, Canada, one of the best-performing members of the G-7, remains on firmer footing. However, this country still has to cope with slower growth, cutbacks and aging infrastructure. As this paper argues, reconciling these facts will take creative, non-partisan problem solving, and it is time governments got to work. Particularly brave politicians might consider charging the public the full costs of infrastructure use in the form of a tax. For the less daring, advances in robotics and data management offer substantial efficiency gains. Whichever path Canadian governments choose, they will not travel it alone. The burgeoning power of social media will amplify citizens’ voices and involvement. However, private sector expertise and capital could be just what is needed to ease Canada’s looming infrastructure woes, notably in the form of infrastructure banks (iBanks); cost-effective, streamlined replacements for the tangled mass of programs and departments that currently build, manage and maintain public infrastructure. Such an institution could allow private investment vehicles like bonds, preference shares and mortgage-backed securities to be issued to create capital and to pay back investors as the objects of its investments repaid the capital borrowed. iBanks could raise tricky problems about overlapping jurisdictions and would, in some parts of the country, be a tough sell, but Canada has been lagging badly in transportation innovation and must consider unorthodox solutions.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".