Differentiating Indigenous Peoples from local communities under climate regimes in just energy transition: Implications for the Inuit and Sami Peoples
Bibliographic record
Abstract
The distinction between Indigenous peoples and local communities under climate regimes was initially addressed under the UNFCCC and has recently garnered renewed attention owing to energy policy changes to cope with rapid climate change such as in Alaska. The Montana climate litigation demonstrates the possibility of harmonizing Indigenous peoples and local communities by clearly identifying the unique climate-related indigenous culture and avoiding unintentional confrontation, given the difficulty of making clear distinctions in modern society and garnering sympathy from the local community against litigations that solely seek indigenous rights, but Indigenous peoples themselves may not see the need for harmony. Indigenous and Community Conserved Areas (ICCAs) is one approach to achieve a 30 by 30 target under the CBD by designating Indigenous peoples’ original land but may not be beneficial to assert inherent rights or the best way to achieve harmony if they work with local communities. The local community of Finnish non-Sami reindeer herders will not receive protection under the ICCPR unlike Sami herders in Norway, if a case similar to Norway’s Fosen case occurs. As just energy transition alone does not automatically reflect the perspectives of Indigenous peoples, green colonialism could easily occur when their rights are not considered.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".