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Record W4224286523 · doi:10.1163/22116427_013010003

From Victims to Contributors: A Human Rights Approach to Climate Change for the Indigenous Peoples of the Arctic

2022· article· en· W4224286523 on OpenAlexaboutno aff
Yuko Osakada

Bibliographic record

VenueThe Yearbook of Polar Law Online · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousHuman rightsClimate changePolitical scienceDeclarationState (computer science)United Nations Framework Convention on Climate ChangeConventionEnvironmental ethicsIndigenous rightsTraditional knowledgePolitical economyLawSociologyEcologyKyoto Protocol

Abstract

fetched live from OpenAlex

Abstract A human rights approach to climate change, which has been claimed by the Indigenous peoples, consisted of procedural and substantive demands. Their procedural demands have mostly been realized in establishing the Local Communities and Indigenous Peoples Platform and the LCIP Platform Facilitative Working Group ( FWG ), where they can participate on equal footing with state parties. It could be argued that the LCIP Platform and the FWG have empowered Indigenous peoples who have hitherto been perceived as mere victims of climate change by making them contributors who provide their traditional knowledge related to addressing and responding to climate change. By contrast, their substantive demands have been imperfectly accepted. This might be improved in the Platform’s future activities. In doing so, the Inuit leader has pointed out that it is important to distinguish between local communities and Indigenous peoples in the UN Framework Convention on Climate Change ( UNFCCC ) processes. This article will argue its feasibility depends on Indigenous peoples’ further efforts to convince state parties to accept such distinctions based on the applicability of the UN Declaration on the Rights of Indigenous Peoples.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.046
GPT teacher head0.352
Teacher spread0.306 · 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.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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