Climate Change Policy in Manitoba: A Small Province Looking to Punch above Its Weight
Bibliographic record
Abstract
INTRODUCTIONn 2008, Manitoba joined the wave of Canadian provinces and US states taking action on climate change by becoming the first jurisdiction in North America to pass legislation committing to greenhouse gas (GHG) emission reduction targets set by the international Kyoto agreement.In the absence of leadership on climate change from federal governments in Canada and the US, Manitoba partnered with and drew lessons from subnational trailblazers like California, British Columbia and Quebec while pursuing several climate change policies that were spreading across the continent.In addition to legislated GHG targets, these initiatives included a regional cap-and-trade system, new standards to reduce emissions from vehicles and fuel, and a template for organizing government to develop policy.When introducing Manitoba's climate change legislation, Premier Gary Doer was so confident in the province's ability to meet its commitments that he suggested the government should be defeated in the next election if its GHG targets were not achieved (Turenne 2008).However, the government was unable to adopt the more aggressive initiatives which it had committed to through collaboration and opted to fall back on measures to reduce coal use and promote renewable energy which it began developing in the early 2000s.This article seeks to explain the climate change policies that resulted in Manitoba by employing three research questions: What motivated the province to engage in collaboration and pursue policies that were spreading across North America?What role did collaboration and cross-jurisdictional learning play in provincial policy development, and how was the selection of 1 Brendan Boyd holds a Ph.D. in public administration from the University of Victoria.I
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".