Bibliographic record
Abstract
Drawing on abundant fossil fuels endowments, Canada has built one of the most carbon-intensive economies in the world. Within the Canadian federation, provincial governments control the vast majority of natural resources, including both hydro-electric potential and fossil fuels. However, the uneven distribution of those resources has yielded tremendous variation in the carbon intensity of provincial economies, and equally great variation in provincial governments’ climate ambitions. In this chapter, I identify three phases in Canadian climate federalism. From 1990 to 2006 a ‘joint decision trap’ prevailed in which the most fossil fuel-dependent provinces vetoed national solutions. From 2007 to 2015 a truncated innovation and diffusion dynamic emerged in which provincial leaders adopted ambitious and sometimes innovative climate policies. However, fossil fuel-dependent provinces did not follow their lead. Emissions reductions hard won by provincial leaders were undone by emissions growth by their recalcitrant neighbours. The third phase, since 2016, is characterised by federal unilateralism. While the mere threat of federal action initially yielded provincial collaboration in an ambitious pan-Canadian climate plan, successful implementation ultimately turned on the federal government’s willingness to follow on that threat. I conclude that, on balance, federalism has exacerbated the challenge of climate action in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".