Technocratic Structures of Climate Policy: Dead-end Debates, Neat Narratives and Manipulative Machiavellianism
Bibliographic record
Abstract
The contemporary models of climate change policy-making in the United States are particular to this decade. The increased role for experts and expert-led policymaking is unprecedented. However this power has been paradoxical. This paper argues that an excessive role for science in discussions of climate change has undermined the public’s role, and has thus undermined the efforts on behalf of policymakers to pass comprehensive climate change policy. Two main aspects of the excessive role for science in the formation of climate policy were found to be 1. the large influence of dissenting scientists on the debate, and 2. the alienation of the public from the discourse. Further, possible scenarios for policymaking, which better balance the roles of experts, the public, and policymakers, are discussed and frameworks for the future are outlined.
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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 | 0.032 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.106 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| 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".