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Record W1997804875 · doi:10.1177/0020852306061629

American hesitations to reduce greenhouse gas emissions: an institutional interpretation

2006· article· en· W1997804875 on OpenAlexaff
Jean Mercier

Bibliographic record

VenueInternational Review of Administrative Sciences · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsKyoto ProtocolGreenhouse gasGovernment (linguistics)Protocol (science)Interpretation (philosophy)State (computer science)Public administrationPolitical scienceEconomicsComputer science

Abstract

fetched live from OpenAlex

In 2005, the objectives of the Kyoto Protocol appear somewhat out of reach, even if Russia gave life to the protocol by signing it in 2004. Even if implemented, the protocol entails huge operational problems. Will the United States prove to be the first country to realize the difficulties in implementing Kyoto, or did they refuse to ratify it for reasons that are very particular to their own institutions? This article is an attempt at supporting the latter proposition. In March 2001, the US government announced that it was withdrawing from the Kyoto Protocol on the reduction of greenhouse gases (GHG) and it has not replaced this participation with a credible program of greenhouse gas reduction. This decision could be analyzed through different angles. In this article, we would like to look at these hesitations through an institutional angle, through the American institutions themselves. Few elements from their institutional and historical past prepare the United States to initiate a vigorous program of GHG reduction, other than through technological innovation or voluntary actions. Even though the institutional concept of path dependency is identified as the concept most helpful in explaining, from an institutional point of view, these hesitations, other institutional explanations are called upon to explain and understand these decisions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.166
GPT teacher head0.403
Teacher spread0.236 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2006
Admission routes1
Has abstractyes

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