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Record W3011693008 · doi:10.1016/j.cosust.2020.01.007

Indigenous environmental justice and sustainability

2020· article· en· W3011693008 on OpenAlexafffund
Deborah McGregor, Steven M. Whitaker, Mahisha Sritharan

Bibliographic record

VenueCurrent Opinion in Environmental Sustainability · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousLegitimacyEconomic JusticePolitical scienceEnvironmental ethicsPoliticsSustainabilityCorporate governanceOrder (exchange)Environmental governanceEnvironmental justiceIndigenous rightsEnvironmental lawSociologyLawPolitical economyEcologyBusiness

Abstract

fetched live from OpenAlex

A distinct formulation of Indigenous environmental justice (IEJ) is required in order to address the challenges of the ecological crisis as well the various forms of violence and injustices experienced specifically by Indigenous peoples. A distinct IEJ formulation must ground its foundations in Indigenous philosophies, ontologies, and epistemologies in order to reflect Indigenous conceptions of what constitutes justice. This approach calls into question the legitimacy and applicability of global and nationstate political and legal mechanisms, as these same states and international governing bodies continue to fail Indigenous peoples around the world. Not only do current global, national and local systems of governance and law fail Indigenous peoples, they fail all life. Indigenous peoples over the decades have presented a distinct diagnosis of the planetary ecological crisis evidenced in the observations shared as part of Indigenous environmental declarations.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.034
Scholarly communication0.0060.006
Open science0.0010.010
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.030
GPT teacher head0.339
Teacher spread0.309 · 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

Citations298
Published2020
Admission routes2
Has abstractyes

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