Leading with Indigenous Principles: Ecological Watershed Governance in British Columbia, Canada
Bibliographic record
Abstract
Both Indigenous and state governments grapple with drivers of ecological decline in colonial states. Ecological drivers such as resource extraction and climate warming meet in the realm of shared land and water governance. Shared governance at the watershed scale must face the challenge of bridging the governance approaches of nation states and Indigenous peoples with distinct legal and governance traditions. I approach that challenge with a case study of the Cowichan Watershed in British Columbia, Canada. I draw on 12 years of shared Indigenous and regional (state) governance experience at the watershed scale. I ask what the Cowichan experience reveals about how a Provincial level state government can enable the full expression of Indigenous governance principles in ecological watershed governance. I adopt a research framework of watershed governance functions and Hul’qumi’num governance principles to investigate how governance works in practice, the influence of Indigenous governance principles, and the roles of government in enabling and impeding those principles. I find that Hul’qumi’num principles are the key driver of success in setting aspirational targets for ecological conditions of the Watershed, in improving decision-making affecting watershed ecology, and in aligning responsibility with authority. The governance of river flow improved greatly over the study period, but the governance of forestry, where there was no opportunity to apply Hul’qumi’num principles, did not. The well-being of salmon improved greatly over the study period, but the well-being of the forest, as measured by old growth retention and recruitment, did not. The contrasts bear witness to the influence of Hul’qumi’num governance principles and to the essential role of Cowichan Tribes leadership. Cowichan Tribes led ecological governance initiatives by applying the principles in accordance with the Hul’qumi’num legal tradition, and by teaching others how to apply the principles. The implications for the Province of British Columbia are that it must change its legislation respecting land and water management, particularly with respect to forestry on large private land holdings, and it must co-create and fund watershed-scale governance entities with Indigenous peoples to enable the application and reap the benefits of Indigenous governance principles.
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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 | 0.001 | 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 | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".