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Record W4413378084 · doi:10.56734/ijahss.v6n8a12

The Evolution Of Global Environmental Politics As A Field Of Inquiry Over The Past Three Decades

2025· article· en· W4413378084 on OpenAlexaff
Eric W. Cheng

Bibliographic record

VenueInternational Journal of Arts Humanities & Social Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsPoliticsField (mathematics)Political scienceEnvironmental politicsEnvironmental ethicsGeographyLawPhilosophy

Abstract

fetched live from OpenAlex

Global environmental politics (GEP) has evolved significantly over the past three decades, emerging as a distinct interdisciplinary field that transcends traditional frameworks in international relations. This paper traces the development of GEP from its state-centric origins in the 1970s and 1980s, through its institutionalisation at the 1992 Rio Earth Summit, to its contemporary focus on justice, equity, and plural governance. The paper highlights key innovations, including the integration of Earth system science, critiques of neoliberal environmentalism, and the centrality of climate change research, while noting persistent challenges such as North-South inequities and the marginalisation of non-climate issues. The field’s expansion to incorporate non-state actors, Indigenous knowledge, and critical methodologies reflects its maturation into a transformative domain addressing Anthropocene challenges. However, tensions remain between incremental reform and systemic change, as well as between academic rigour and policy relevance. This analysis underscores GEP’s vital role in reimagining global governance for an era of ecological crisis.

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)0.013
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.017
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.008
Science and technology studies0.0070.040
Scholarly communication0.0170.014
Open science0.0010.009
Research integrity0.0060.008
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 head0.019
GPT teacher head0.329
Teacher spread0.310 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueInternational Journal of Arts Humanities & Social ScienceSame topicTransboundary Water Resource ManagementFrench-language works237,207