From Victims to Contributors: A Human Rights Approach to Climate Change for the Indigenous Peoples of the Arctic
Bibliographic record
Abstract
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.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".