Self-reinforcing and self-undermining feedbacks in subnational climate policy implementation
Bibliographic record
Abstract
This study demonstrates how interpretive feedback functions as an intervening mechanism during policy implementation that helps explain variation in subnational climate policy entrenchment. We examine three interrelated climate policy processes in Ontario, Canada from 2001–2018: a coal phase-out (2001–2014), the feed-in-tarriff (FIT) program for renewable energy (2006–2013) and a cap-and-trade program (2008–2018). Successful framing of the coal phase-out in terms of gains for both public health and climate change helped generate a broad-based coalition of support during implementation. Conversely, we find that the FIT and the cap-and-trade programs were vulnerable to framing around losses, especially regarding electricity rates and household costs, which counter-coalitions used to weaken public support during implementation. Our analysis demonstrates that building supportive coalitions for climate policy goes beyond the material gains and losses generated by initial policy designs. Framing strategies interact with policy designs over time to support or undermine policy durability.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".