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Critical Perspectives on Representation, Equity, and Rights Developing a Comparative Politics of Environmental Justice

2021· book-chapter· en· W4213333458 on OpenAlexaff
Kimberly R. Marion Suiseeya

Bibliographic record

VenueOxford University Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsScience North
Fundersnot available
KeywordsEnvironmental justicePoliticsEnvironmental governanceEnvironmental politicsPolitical scienceEnvironmental studiesEnvironmental ethicsEnvironmentalismEquity (law)SovereigntyEconomic JusticeIndigenousCorporate governanceLawEcologyBusiness

Abstract

fetched live from OpenAlex

Abstract The politics of environmental justice increasingly feature in environmental governance across multiple levels. Environmental defenders risk their lives to protect land, water, and forests. Non-human actors like rivers are gaining rights. Frontline environmental justice communities now include nation-states like Fiji that faces existential threats from climate change. Indigenous Peoples’ fights for self-determination illuminate how deeply connected and inseparable are the politics of sovereignty, representation, and environment. This chapter explores these developments to chart and examine how a politics of environmental justice can inform environmental and social policies by treating environmental justice as a driver, rather than unintended consequence, of policy and politics. Through this critical, comparative review, the chapter illuminates how and why environmental justice concerns matter for environmental governance and for the study of comparative environmental politics.

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)0.006
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 score0.010
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0050.032
Scholarly communication0.0100.012
Open science0.0010.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0070.000

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 head0.081
GPT teacher head0.343
Teacher spread0.261 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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