Knowledge co-creation through Indigenous arts: Diversity in freshwater quality monitoring and management
Bibliographic record
Abstract
In this paper, we recognize the need to diversify knowledge systems in freshwater quality monitoring. We acknowledge the importance of Canadian-Indigenous reconciliation and build on two recommendations from past work: (1) to recognize different forms of knowledge (including Indigenous and non-Indigenous community knowledge) and (2) to facilitate action by managers and decision-makers. Using a co-creative process, an artistic (i.e., art-based) research method was used to engage Indigenous youth in conversations about their relationships with the Grand River watershed (Ontario, Canada). We present six lessons learned from co-creating our process and six recommendations for those who hope to implement a similar approach. A list of 10 principles and values to guide water quality monitoring demonstrates how the collective perspectives of Indigenous youth and current water monitoring and management practitioners may be applied. Finally, we highlight three important factors for implementing such an approach as: relationship-building, capacity building, and reciprocation.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.037 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".