“Saying No to Resource Development is Not an Option”: Economic Development in Moose Cree First Nation
Bibliographic record
Abstract
In 2004 and 2005, the Supreme Court of Canada handed down a trilogy of decisions that outlined the doctrine of the duty to consult and accommodate, thereby changing how resource development occurs in Aboriginal traditional territories. As a result of these decisions, new avenues of economic development for well-resourced First Nations have opened up, with the hope of creating a new future for remote Aboriginal communities; but are these types of agreements meeting the expectations of First Nations and their members? The authors visited a First Nations community that recently negotiated impact and benefit agreements with large industrial proponents. The authors conducted in-depth, long interviews with 17 key informants: former chiefs and grand chiefs, executive directors of community agencies, program directors, business persons, spiritual persons and elders, property managers, and direct-service practitioners. Five themes, or areas of concern, emerged from the research: unemployment, employment, and economic stimulation; social and physical health concerns; negotiations and meaningful community involvement; corporate social responsibility, capacity building, and social capital; and environmental concerns and cultural relevance. Despite the concerns these agreements raised, 14 of 17 informants remained in favour of the impact and benefit agreements.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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".