Bibliographic record
Abstract
Abstract In debates surrounding policy options for mitigating greenhouse gas (GHG) emissions, economists of various political stripes are near unanimous in their advocacy of putting a price on carbon, whether through a tax or emissions trading program. Due to the visible costs imposed on industry and consumers, however, these policies have been resisted by carbon-intensive industries and by an ideologically divided public, producing incentives for vote-seeking politicians to avoid implementing comprehensive and stringent carbon prices within their own borders. In this highly politicized environment, and considering the more recent diffusion of market-based instruments across political jurisdictions around the world, researchers have sought to identify the conditions most favorable to implementing carbon taxes and cap-and-trade programs, the correlates of public support for these policies, and the extent to which different communication strategies may help build public support. How do experts, political leaders, and members of the public understand these policy instruments, and what specific approaches have been most successful in persuading policy makers and the public to support a price on carbon? In places that have yet to implement a carbon price, what can communication strategists learn from existing research and the experience of other jurisdictions where such policies have been successfully implemented? In places where carbon taxes or carbon cap-and-trade programs exist, how are the benefits of these policies best communicated to ensure the durability of carbon pricing policies over time?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".