Litigating climate change in the Arctic: the potential of Sámi human rights claims
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.022 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".