Indigenous Peoples, Natural Resources and Governance : Agencies and Interactions
Bibliographic record
Abstract
This book offers multidisciplinary perspectives on the changing relationships between states, indigenous peoples and industries in the Arctic and beyond. It offers insights from Nordic countries, Canada, Australia, New Zealand and Russia to present different systems of resource governance and practices of managing industry-indigenous peoples’ relations in the mining industry, renewable resource development and aquaculture. Chapters cover growing international interest on Arctic natural resources, globalization of extractive industries and increasing land use conflicts. It considers issues such as equity, use of knowledge, development of company practices, conflict-solving measures and the role of indigenous institutions. Focus on Indigenous peoples and Governance triangle Multidisciplinary: political science, legal studies, sociology, administrative studies, Indigenous studies Global approach: Nordic countries, Canada, Russia, Australia, New Zealand and Canada Thorough case studies, rich material and analysis The book will be of great interest to legal scholars, political scientists, experts in administrative sciences, authorities at different levels (local, regional and nations), experts in human rights and natural resources governance, experts in corporate social governance.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".