Indigenous Rights and Environmental Governance: Lessons from the Great Bear Rainforest
Bibliographic record
Abstract
In British Columbia, conflicts over First Nations rights to natural resource management have become a common feature of the political landscape. A range of emerging issues — such as private hydroelectric developments, a resurgent mining industry, oil and gas exploration, and proposed pipelines — combine with increasingly robust legal grounds for First Nations rights to suggest that significant challenges to effective regimes of environmental governance loom on the horizon, as does their necessity. This article examines the negotiations that led to the novel forms of environmental governance that are being deployed in the central and north coast of British Columbia, also known as the Great Bear Rainforest. The negotiation processes, which included groundbreaking “government-to-government” negotiations between First Nations and the BC government, signal a significant shift in the way First Nations are involved in land-use decisions in British Columbia. The article considers the character of these negotiations, exploring what their wider implications and applicability might be for First Nations, the environmental movement, and the provincial government. Data were collected through semi-structured interviews, with individuals involved directly or indirectly in the negotiations; the analysis of the interview data situates interviewee insights within a wider consideration of strategies for achieving forms of environmental governance that are responsive to Aboriginal peoples’ rights. While many challenges remain in implementing the outcomes of the Great Bear Rainforest Agreements, important lessons can be learned from the processes that were used to reshape the future of this region.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".