Bibliographic record
Abstract
Abstract The phase-out of coal-fired electricity production in the Canadian Province of Ontario has been widely described as one of the most significant measures taken by any government in the world to reduce greenhouse gas (GHG) emissions. The phase-out of coal, which in the early 2000s constituted a quarter of the province’s electricity supply, was completed in 2014. The phase-out was associated with dramatic improvements in air quality in the southern part of province. At the same time, Ontario’s approach to the phase-out involved a series of significant environmental, economic, and political trade-offs, the benefits of which continue to be debated, and whose consequences have affected the province’s politics profoundly. The chapter examines the evolution of the role of coal-fired electricity in Ontario, the emergence of the concept of a phase-out, and the factors that contributed to its ultimate implementation. Within McConnell’s (2010) framework for assessing policy outcomes around programmatic results, policy processes, and politics, the chapter concludes that outcomes of the coal phase-out process range from a resilient and political success in terms of the phase-out itself, to a political failure with respect to the McGuinty (2003–2013) and Wynne (2013–2018) governments’ overall handling of electricity policy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".