Extractive Industry and the Sustainability of Canada's Arctic Communities
Bibliographic record
Abstract
Modern treaties, increased self-government, new environmental assessment rules, co-management bodies, and increased recognition and respect of Indigenous rights make it possible for northern communities to exert some control over extractive industries. Whether these industries can increase the well-being and sustainability of Canada’s Arctic communities, however, is still open to question. Extractive Industry and the Sustainability of Canada’s Arctic Communities delves into the final research findings of the Resources and Sustainable Development in the Arctic project which attempted to determine what was required for extractive industry to benefit northern communities. Drawing on case studies, this book explores how northern communities can capture and distribute a fairer share of financial benefits, how they can use extractive activities for business development, the problems and possibilities of employment and training opportunities, and the impacts on gender relations. It also considers fly-in fly-out work patterns, subsistence activities, housing, post-mine clean-up activities, waste management, and ways of monitoring positive and negative impacts. While extractive industries could potentially help improve the sustainability of Canada’s Arctic, many issues stand in the way, most notably power imbalances that limit the ability of Indigenous Peoples to equitably participate in their governance. Extractive Industry and the Sustainability of Canada’s Arctic Communities emphasizes the general need to determine how new institutions and processes, which are largely imported from the south, can be adapted to allow for a more authentic participation from the Indigenous Peoples of Canada’s Arctic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".