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Record W3080885928 · doi:10.1007/s10584-020-02826-y

Arctic Athabaskan Council’s petition to the Inter-American Commission on human rights and climate change—business as usual or a breakthrough?

2020· article· en· W3080885928 on OpenAlexaboutno aff
Agnieszka Szpak

Bibliographic record

VenueClimatic Change · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousCommissionObligationHuman rightsPolitical scienceArcticIndigenous rightsLawClimate changeEcology

Abstract

fetched live from OpenAlex

Abstract In 2013, the Arctic Athabaskan Council representing the Arctic Athabaskan peoples filed a petition to the Inter-American Commission on Human Rights. The Council sought relief for violations of their rights resulting from rapid Arctic warming and melting caused by emissions of black carbon by Canada. The aim of the paper is to show legal complaints and arguments of a particular indigenous people, Arctic Athabaskans—arguments intended to enforce Canada’s obligation to reduce or eliminate black carbon emissions, which negatively affect numerous rights of indigenous Athabaskans. Additionally, the article will point to the new legal developments and potential success of those arguments and litigation itself. The article analyses issues at the intersection of human rights, indigenous peoples and climate change. The concluding remarks attempt to answer the research questions and offer some reflections on the potential to protect indigenous peoples’ rights offered by this type of advocacy strategy and, more specifically, the petition in particular. The research method adopted is that of legal-institutional analysis as well as content analysis of relevant literature (analysis of the discourse). This paper moves forward existing climate litigation literature which focuses on human rights. As Osofsky and Peel (2018) highlight, human rights-based climate litigation is a new development in the field, and this paper expands it further.

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 score0.910
Threshold uncertainty score0.990

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.202
GPT teacher head0.375
Teacher spread0.172 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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