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Record W2164669565 · doi:10.5296/jpag.v2i4.2800

Technocratic Structures of Climate Policy: Dead-end Debates, Neat Narratives and Manipulative Machiavellianism

2013· article· en· W2164669565 on OpenAlexfundno aff
Taylor A. Murray

Bibliographic record

VenueJournal of Public Administration and Governance · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsnot available
FundersKillam TrustsFulbright Canada
KeywordsDissenting opinionMachiavellianismClimate changeTechnocracyPolitical sciencePolitical economyNarrativePublic policyAlienationBalance (ability)Power (physics)Environmental ethicsPublic administrationSociologyPoliticsLawSocial psychologyEcologyPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0130.106
Scholarly communication0.0190.022
Open science0.0020.011
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.177
GPT teacher head0.401
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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