Sustainable Transportation: Theory or Practice? Perspective of Planners and Policy Makers
Bibliographic record
Abstract
In light of the rapid growth that Canadian urban areas are experiencing, sustainable development objectives have been at the forefront of planning initiatives. Political debates have focused on sustainable growth and environmental preservation while public awareness of the same issues has notably increased. Awareness of - and planning for - sustainability surely constitutes a step in the right direction; however, it does not bear much benefit if it is not matched by funding and implementation. This paper examines whether the current prevalence of the “sustainability terminology” is merely an indication of the correct political jargon being adopted or a real sign that Canadian urban areas are becoming more sustainable. A questionnaire-based survey is conducted with planners and policy-makers at the three levels of government (municipal, provincial, federal). Some of the issues discussed include the existing status of funding and implementation of sustainable transport plans and policy appraisal in terms of sustainability objectives. Results show that the progress in thinking and crafting of plans at the urban level has not been matched by increased funding for implementation. As a result, frustration among current planners has become widespread translating into a gloomy outlook on the future of Canadian cities in the next 20-25 years.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".