A Framework to Guide Thinking and Analysis Regarding Climate Change Policies
Bibliographic record
Abstract
The potential impacts from climate change, and climate change policies, are massive. Careful thinking about what we want climate change policies to achieve is a crucial first step for analysts to help governments make wise policy choices to address these concerns. This article presents an adaptive framework to help guide comparative analysis of climate change policies. The framework recognizes the inability to forecast long-term impacts (due in part to path dependance) as a constraint on the use of standard policy analysis, and stresses learning over time as a fundamental concern. The framework focuses on the objectives relevant for climate change policy in North America over the near term (e.g., the next 20 years). For planning and evaluating current climate policy alternatives, a combination of fundamental objectives for the near term and proxy objectives for characterizing the state of the climate problem and the ability to address it at the end of that term is suggested. Broad uses of the framework are discussed, along with some concrete examples. The framework is intended to provide a basis for policy analysis that explicitly considers the benefits of learning over time to improve climate change policies.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".