Heating Up, Backing Down: Evaluating recent climate policy progress in Canada
Bibliographic record
Abstract
This is a co-publication by the Canadian Centre for Policy Alternatives (CCPA) and the Adapting Canadian Work and Workplaces to Respond to Climate Change research program (ACW). It assesses the climate policy progress of Canadian governments over the past two years with respect to long-term greenhouse gas emission reductions and concludes that positive progress in British Columbia and Quebec over the past few years is outweighed by backsliding in other provinces. The new governments in Alberta and Ontario—Canada’s two biggest carbon polluters—have reversed the climate policies of previous governments, which puts Canada’s already-unlikely national targets even further at risk. \nThe report identifies two growing threats to climate policy progress in Canada: \n1. A narrow public debate over carbon pricing is eroding political will for a more comprehensive climate policy approach. There are many other policies that are less controversial and can be just as effective at reducing emissions. \n2. Canadian governments have been unwilling to introduce supply-side energy policies designed to restrict the production of fossil fuels, even though keeping much of our oil and gas in the ground is necessary to avoid the worst effects of global climate breakdown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".