The Municipal Role in Climate Policy
Bibliographic record
Abstract
Climate change is a national and international issue. Nevertheless, municipalities around the world have cemented themselves as key players in reducing emissions and adapting to the increase in extreme weather events. The third report in the Who Does What series focuses on the role of Canadian municipalities play in the fight against climate change, and how that role can complement and be supported by other orders of government. Jennifer Winter notes that municipalities have limited direct control over emissions within their boundaries, but can have large indirect effects due to their substantial populations. She lays out a series of policies that municipalities can work toward – both independently and in cooperation with other orders of government – from buildings to transportation, waste to land use. Elliott Cappell argues that, when it comes to enhancing climate resilience, Canadian municipalities are often both too big and too small: too big to tackle hyper-local issues but too small to address issues at scale. He argues for ways municipalities can break down silos to better confront the climate challenge, and how the provinces and federal government can best support municipal action. Sadhu Johnston concludes with six recommendations to all orders of government that would help improve action and collaboration in climate policy. The recommendations range from addressing the skills gap at the municipal level through a climate education program to implementing provincial mandates requiring municipal climate plans and enhancing intergovernmental cooperation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 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".