The space left for Indigenous peoples' voices in Canadian and Fennoscandian mining legal frameworks
Bibliographic record
Abstract
In recent decades, mining activities have intensified in many regions of the world, leading to conflicts between Indigenous communities and mining proponents. These conflicts are occurring in the context of growing recognition of the rights of Indigenous peoples at the national and international levels. Using a comparative law approach, this chapter analyzes how and to what extent the rights of Indigenous peoples are recognized and protected when they run up against mining legal frameworks in Canada and the Fennoscandian countries (Norway, Sweden, and Finland). Following a brief overview of the foundations, sources, and modes of the legal recognition and protection of Indigenous peoples’ territorial rights in the jurisdictions studied, it describes the mining legal frameworks and specific forms that Indigenous peoples’ rights take when intersecting with these frameworks in each country. Despite recent changes to the mining legal frameworks in Canada and Fennoscandia to account for Indigenous peoples’ rights, this study shows that in all of these jurisdictions, mining development is still largely prioritized over land uses by Indigenous peoples, whose pathways to participation in formal mining decision-making processes remain narrow and limited. This chapter concludes that the increased recognition of Indigenous peoples’ rights has not fundamentally altered the preexisting structures and rationales of the mining legal frameworks in the countries studied.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.031 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".