Navigating Reconciliation through Cultural Flows for Industrialized Free-Flowing Rivers
Bibliographic record
Abstract
Cultural flows are an emergent water policy tool gaining recognition for their potential to overcome the continued marginalization of Indigenous peoples’ interests in Canadian freshwater governance, but quantified cultural flows are rarely adopted by state governments. Using community-based participatory research and leveraging an Ethical Space Framework, this research provides practical insight into the adoption of cultural flows in ways mutually acceptable to state governments and Indigenous peoples. The practical insight was gained by demonstrating the significance of a quantified cultural flows example termed Aboriginal Navigation Flows from Alberta and the institutional influences on its adoption by a state government. Data collected through documents and interviews revealed that ANF were significant because they translated an Indigenous conception of wellness connecting river navigability, boating, human relationships, human-waterscape relationships, Indigenous rights, and self-determined change adaptation. These insights into ANF significance showed how cultural flows could meaningfully shape freshwater governance in which environmental flow assessments for free-flowing rivers are undertaken. Data collected through documents and interviews and analyzed using the Implementing Innovation Framework revealed that structural institutions critically influenced ANF adoption. Joint communications by collaborating Indigenous peoples worked to overcome state government resistance grounded in vested economic interests. To reshape structural institutions, cultural drivers of ANF adoption could be better leveraged by overcoming individual barriers to ANF adoption. Collectively, these insights into ANF adoption show how freshwater governance arenas may become ethical spaces.
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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 | 0.006 | 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 | 0.009 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".