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Record W2062585512 · doi:10.1177/0268580904040922

Building Environmental States

2004· article· en· W2062585512 on OpenAlexaff
Scott Frickel, Debra J. Davidson

Bibliographic record

VenueInternational Sociology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRationalization (economics)SustainabilityLegitimacyEnvironmental governanceCorporate governanceCivil societyPoliticsEnvironmental justicePolitical scienceSociologyPolitical economyEconomic systemEconomicsLawEcology

Abstract

fetched live from OpenAlex

This article explores the potential for nation-states to become substantial contributors to sustainability governance. This potential resides in the ability of nation-states to make environmental protection a basic goal, in part by committing institutional resources toward the formation and implementation of substantive actions perceived necessary for long-term environmental sustainability. Existing research suggests that nation-states undertake environmental action in order to maintain legitimacy in the face of political pressure. While the maintenance of legitimacy is necessary, we argue that a substantive state role in sustainability governance is also dependent upon the rationalization of state environmental roles. Further, rationalization can be fostered through the enrichment of embedded state-societal networks with two key actors in civil society: environmental justice movements and environmental knowledge professionals. This article develops a conceptual framework that grounds sustainability efforts in rationalization processes and examines the synergistic potential for these two social actors to help build states that institute fundamental environmental reform.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0060.009
Open science0.0010.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.002

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 head0.018
GPT teacher head0.342
Teacher spread0.324 · 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

Citations44
Published2004
Admission routes1
Has abstractyes

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