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Record W3149014355

Human rights, environmental justice, and the North-South divide

2015· article· en· W3149014355 on OpenAlexaboutno aff
Carmen G. González

Bibliographic record

VenueChapters · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsEnvironmental justiceInjusticeGrassrootsPolitical scienceRights of NatureFraming (construction)International human rights lawEnvironmental lawEnvironmentalismEnvironmental degradationClimate justiceEnvironmental governanceIndigenous rightsRight to propertyEnvironmental ethicsDevelopment economicsLawCorporate governanceGeographyClimate changeEconomics
DOInot available

Abstract

fetched live from OpenAlex

From the Ogoni people devastated by oil drilling in Nigeria to the Inuit and other indigenous populations threatened by climate change, communities disparately burdened by environmental degradation are increasingly framing their demands for environmental justice in the language of environmental human rights. However, some scholars have expressed scepticism about the environmental human rights project. First, they remind us that the human rights governance capacity of many states in the global South has been compromised by the neoliberal economic reforms imposed by the International Monetary Fund and the World Bank as well as by trade and investment agreements. Second, they question the ability of human rights law to adequately articulate and advance the aspirations and resistance strategies of diverse grassroots social justice movements, and warn us about the susceptibility of human rights law to co-optation by powerful Northern states. This Chapter examines the promise and the peril of environmental human rights as a means of challenging environmental injustice within nations and the North-South dimension of environmental injustice.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.905
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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