Bibliographic record
Abstract
Abstract Political scientists Alexander Gazmararian and Dustin Tingley's incisive new book Uncertain Futures: How to Unlock the Climate Impasse contends that credibility is key to unlocking the deadlock over climate policy. They claim that fossil fuel communities have often been skeptical of any transition away from fossil fuels with good reason. In similar situations, policymakers have often failed to follow through on policies meant to mitigate economic dislocation. Drawing on a wealth of quantitative and qualitative evidence from energy-producing communities, including surveys of residents and officials alike, Gazmararian and Tingley find that different policy features that bolster credibility can build support for a transition to clean energy sources. The book provides a much-needed view of the energy transition from the ground-up. Yet the book pays less attention to a principal-agent problem at the heart of the clean energy transition: many of the elected representatives of the communities most affected by the transition don’t acknowledge any need for a transition. What's more, in a highly polarized environment, the impact of policy feedbacks is likely to be muted. Drawing on the experiences of the ACA and Canada's carbon tax, we suggest that even when the policy features that the authors propose are present, support for clean-energy policies may not rise dramatically.
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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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".