Developing a Climate Change Mitigation Policy Inventory for Canada
Bibliographic record
Abstract
Across orders of governments, jurisdictions are continually expanding their implementation of policies to reduce greenhouse gas (GHG) emissions and mitigate the impacts of climate change. In federations such as Canada, the mix of policies used is further complicated by overlapping regulation both within and across federal and provincial or territorial governments. Canada's climate policy landscape is marked by variation in timing, effort, and approach and driven by differences among provinces and territories in economic structures, political ideologies, energy resources, and emissions. However, a clear picture of the wide-ranging efforts undertaken across jurisdictions remains unavailable. To address this gap, we have developed a comprehensive and dynamic inventory of climate policies in Canada. This article outlines the steps taken to establish the inventory of 341 climate policies in Canada, the coding protocol used to assess policy design elements, and a description of the inventory findings. By shedding light on the complex web of climate policies in Canada, this inventory aims to provide researchers and policy-makers with a clear picture of the ongoing efforts to reduce GHG emissions. It also seeks to inform future research on the impacts of and interactions among policy tools in achieving a range of societal objectives.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".