Understanding collaborations and community partnerships in aquatic research: a review of literature from the Bow River Basin, 2014–2024
Bibliographic record
Abstract
This systematic review examines peer-reviewed literature (2014–2024) on aquatic health research in Alberta’s Bow River Basin to assess the inclusion of Indigenous Knowledge and collaboration in Western scientific studies. Despite growing recognition of the value of Indigenous ways of knowing, such as holistic stewardship and place-based practices, our analysis of 69 regional publications reveals minimal meaningful co-creation with Indigenous communities. Most studies relied on disciplinarily siloed, quantitative endpoints (e.g., water quantity modelling), with only one study explicitly co-developing research methods and outcomes with Indigenous partners (Siksika Nation). A parallel review of National Sciences and Engineering and Research Council grants (2020–2024) showed relative increases in approvals for community-based research, yet corresponding peer-reviewed outputs remain scarce. This disparity highlights some potential systemic barriers to meaningful collaboration, such as rigid academic timelines and tokenistic “integration” of Indigenous Knowledge. Key recommendations include mandatory Indigenous data stewardship training, flexible funding structures to support relationship-building, and recognition of nontraditional research deliverables. The findings underscore the urgent need for equitable partnerships that center Indigenous voices from project inception to dissemination, ensuring culturally relevant and sustainable water management in the Bow River Basin and more broadly.
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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 | 0.025 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".