Enabling Indigenous-centred decision-making for a just energy transition? Lessons from community consultation and consent in the circumpolar Arctic
Bibliographic record
Abstract
Governance and decision-making that uphold the rights, interests, knowledges, and values of Indigenous peoples and land-connected communities are increasingly recognised as critical components of a just energy transition. Despite the unprecedented inclusion of Indigenous peoples in resource governance, it is unclear how community consultation and consent can effectively support Indigenous-centred decision-making. In this paper, we provide an integrative and case review of community experiences with consultation and consent across the Arctic and sub-Arctic region which along with other ‘resource geographies’ are increasingly affected by transition minerals mining and renewable energy infrastructure. Key themes identified in the review include: (1) limitations of state- and company-led community consultation and consent; (2) practices of Indigenous-centred (Indigenous-led, Indigenous-benefiting and Indigenous-informed) decision-making; and (3) barriers to Indigenous-centred decision-making. Focusing on the circumpolar north, this paper contributes to broadening the discussion on just energy transitions for Indigenous peoples. Implications for scholarship and practice are discussed, reflecting on community consultation and consent in the current rush to supply minerals and infrastructure for the global energy transition.
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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 | 0.111 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.038 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.009 |
| 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".