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Comparative Climate Change Policy and Federalism: An Overview

2012· article· en· W1857107429 on OpenAlexaffabout
Douglas M. Brown

Bibliographic record

VenueReview of Policy Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsFederalismClimate changePolitical scienceCorporate governanceContext (archaeology)Climate governanceCollective actionMulti-level governancePoliticsPublic administrationPolitical economyEconomicsGeographyLaw

Abstract

fetched live from OpenAlex

Abstract This commentary provides an overview of the four papers in this issue of Review of Policy Research on the politics of climate change. The papers all address in one way or another aspects of how federal‐type systems are dealing with the collective action and multilevel governance issues of climate change policy. The comparative study of federal systems provides insight into how domestic authority is so often overlapping and divided when dealing with greenhouse gas emissions and climate change. Federal arrangements offer a rich array of norms, institutions, and practices for tackling these problems. Federal systems grapple continuously with the kinds of issues that are the most intractable in the climate change case, such as overcoming interregional differences of interests and values. A common federal feature is competition among subnational governments and between them and national or federated governments over climate change policy, which has been especially significant in the United States and in Canada in the relative absence of national action––although soberingly, the whole is as yet nowhere near as great as the sum of the parts. More significant, but rarer is the achievement of tighter coordination in federal systems achieved through intergovernmental co‐decision, as seen in the European Community and Australia. This has been accomplished in large part due to a consensus among all intergovernmental parties on the nature of the problem and congruence with the existing international regime, characteristics missing in the North American context.

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)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0100.015
Science and technology studies0.0010.004
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.642
GPT teacher head0.641
Teacher spread0.000 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations32
Published2012
Admission routes2
Has abstractyes

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