Water governance and Indigenous governance: Towards a synthesis
Bibliographic record
Abstract
In Canada, Indigenous peoples have sui generis rights and millennia of stewardship on their traditional homelands. However, non-Indigenous understandings of those sui generis rights, let alone knowledge of Indigenous history, Indigenous knowledge, and understanding of Indigenous governance and self-determination goals, is generally poor in Canadian society. This paper explores the conceptual gap that exists between underlying principles, values and norms of Indigenous governance within the specific context of contemporary water governance in Canada. The province of British Columbia, Canada is used as an empirical setting to illustrate the issues considered. In this province, numerous organizations involved are attempting to collaborate with First Nations peoples to address water issues. This paper questions the underlying assumptions in the collaborative governance literature relative to assertions surrounding self-determination found in Indigenous governance scholarship. We conclude (1) that both the scholarship and the practice of water governance do not sufficiently address concerns relating to Indigenous governance, Indigenous pre-and post-colonial history, and varying concepts of self-determination, and (2) the ability of collaborative processes to address current and emerging governance challenges in the water realm depends in part on the extent to which assumptions held by non-Indigenous and Indigenous peoples can be reconciled.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".