Arctic Energy Development and Best Practices on Consultation with Indigenous Peoples
Bibliographic record
Abstract
Arctic energy development has massive potential in helping meet world energy needs and in promoting sustainable Arctic development. At the same time, the Arctic is largely inhabited by Indigenous peoples and has special environmental vulnerabilities that can contribute to impacts on Arctic Indigenous peoples. Norms of consultation with Indigenous peoples thus have a particular importance in Arctic contexts. This Article examines this very much under-studied issue. It seeks to make an innovative contribution to understanding best practices on consultation appropriate to Arctic-specific contexts, considering evolving national and international law norms of consultation. Part II of the Article carries out a comparison of existing implementations of international norms of consultation in countries across the Arctic region. Part III distills best practices on consultation from both evolving national and international law, including both in the Arctic states but also in other states whose practices can shed light. Part IV examines unique Arctic circumstances and develops a set of categories for Arctic-specific consideration of consultation. Part V ties together the best practices and the impact categories of Part IV and seeks to comment on the existing state practices discussed in Part II, signalling directions in which different states might consider shifting so as to best respect consultation norms. The underlying aim of the Article is to offer practical recommendations that facilitate Arctic energy development in responsible ways, thereby furthering its long-term acceptability and potential.
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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 | 0.041 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.039 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".