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Record W4318658250 · doi:10.4337/jhre.2022.02.05

Litigating climate change in the Arctic: the potential of Sámi human rights claims

2022· article· en· W4318658250 on OpenAlexaff
Linnéa Nordlander

Bibliographic record

VenueJournal of Human Rights and the Environment · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsHuman rightsClimate changeEnvironmental lawPolitical scienceThe arcticArcticEnvironmental ethicsEnvironmental planningEnvironmental resource managementLawGeographyEnvironmental scienceOceanographyEcologyBiologyPhilosophy

Abstract

fetched live from OpenAlex

This article explores the prospects of human rights litigation over climate change impacts felt in the Arctic, examining especially whether the impacts experienced by Sámi individuals in the European Arctic can be framed as human rights violations in the context of the European Convention on Human Rights (ECHR). As one of Europe’s few Indigenous peoples, the Sámi are uniquely vulnerable to the adverse effects of climate change, with inhabitants of the Arctic being on the frontlines of experiencing its impacts. Sámi representatives have engaged in unsuccessful mitigation litigation on the basis of these impacts. The present article builds on previous litigation initiatives by exploring an alternative litigation avenue in order to assess its potential. In doing so, the article outlines the ways in which climate change has been reported to interfere with Sámi cultural practices to date. Given that the ECHR does not contain explicit rights to culture or group rights, the article examines whether these impacts can be framed in terms of the individualistic civil and political rights that the Convention covers. The article therefore examines the possibilities of framing these impacts in terms of the prohibition of ill treatment (art 3), the right to respect for private and family life (art 8), and the right to non-discrimination (art 14).

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.278
Teacher spread0.259 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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