COLLABORATIVE WATER GOVERNANCE: FOSTERING PARTICIPATION, RELATIONSHIPS, AND RECONCILIATION IN MISTAWASIS NÊHIYAWAK
Bibliographic record
Abstract
In Canada, water governance is confronted with colonial legacies that historically have marginalized and silenced Indigenous water worldviews, knowledge and needs. Alternative water governance frameworks are needed and demanded by Indigenous Peoples to overcome the complex water issues they face. In this dissertation the meaning of water and water governance from Mistawasis Nêhiyawak First Nation (MNFN) water ontologies and epistemologies is explored and the contributions to collaborative water governance approaches in the North Saskatchewan River Watershed (NSRW), Saskatchewan and Canada are discussed. The importance of balancing power relationships in water decision-making and participation by including collaborative approaches as the theoretical and practical framework for water governance is considered. Collaborative water governance as a constructive process is proposed, where hybrid pathways and strong partnerships between rights holders and stakeholders are co-built, and from this perspective, Indigenous water ontologies, epistemologies and self-determination are legitimized in collaborative water governance arrangements. This dissertation documents the collaborative water governance experience lived by MNFN while overcoming water threats affecting their Nation. The Honour the Water Governance Framework co-built with MNFN as a model founded in MNFN identity, knowledge, and self-determination is presented. This framework highlights shared dialogue and complementarity as key elements for holistic and sustainable water governance approaches. Collaborative water governance arrangements built on trustful relationships and aware of Indigenous Knowledge and self-determination may contribute to meaningful processes of reconciliation needed in Canada. Partnerships built between MNFN and water stakeholders in the NSRW opened pathways for honouring water while healing broken relationships and contributing to transformative reconciliation in the practice. The theoretical and methodological approaches used in this dissertation contribute to practices of decolonization in water governance towards building Nation-to-Nation relationships while developing more sustainable water governance approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".