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Record W2137021312 · doi:10.1177/0192512104045077

The USA and Global Environmental Policy: Domestic Constraints on Effective Leadership

2004· article· en· W2137021312 on OpenAlexaboutno aff
Glen Sussman

Bibliographic record

VenueInternational Political Science Review · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsConventionPolitical scienceSalience (neuroscience)PoliticsGlobal LeadershipEnvironmental politicsPrincipal (computer security)International relationsOrder (exchange)Environmental policyPublic administrationPolitical economyEconomicsEnvironmental resource managementLawPublic relations

Abstract

fetched live from OpenAlex

During the past three decades, global environmental policy has increased in salience in international politics. What role has the USA, a principal actor in global affairs, played in multilateral efforts to promote environmental protection? What factors might account for US actions regarding progress or problems related to global environmental policy? In order to answer these questions, I examine the role of three principal actors in the US political system, namely, the American president, the Congress, and domestic organized interests. This discussion is followed by three case studies (the Montreal Protocol, the Convention on Global Climate Change, and the Convention on Biodiversity) that show the role of these political actors in shaping US global environmental policy. When the USA provides leadership, it bolsters multilateral efforts to address global environmental problems. When it fails to offer leadership, it weakens that effort. Either way, domestic political factors (rather than interstate relations) play a central role in shaping US global environmental policy.

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)0.006
metaresearch head score (Gemma)0.011
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: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0050.006
Scholarly communication0.0120.006
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0190.001

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 head0.109
GPT teacher head0.341
Teacher spread0.232 · 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
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

Citations54
Published2004
Admission routes1
Has abstractyes

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