“Learning Together”: Braiding Indigenous and Western Knowledge Systems to Understand Freshwater Mussel Health in the Lower Athabasca Region of Alberta, Canada
Bibliographic record
Abstract
Fort McMurray Métis Elders and land users have observed a decrease in the population density of freshwater mussels (known locally as clams; Unionidae) in the lower Athabasca region (LAR) in recent decades. A community-based participatory research (CBPR) approach, braided with Indigenous Knowledge, is used as a guiding framework to facilitate partnerships and create safe, ethical spaces across diverse knowledge systems to address questions about freshwater mussel health in a locally relevant and culturally appropriate way. Opportunities for Elders and land users to travel along the Athabasca and Clearwater rivers in search of freshwater mussels allowed for the renewal of personal and cultural relationships to place that was braided with the study of parameters relevant to Western science. Our search revealed the presence of fat muckets (Lampsilis siliquoidea), with a limited number of giant floaters (Anodonta grandis), in our study area. However, delineating the types of species present is only the beginning of our work to understand freshwater mussel health in the LAR. We present a methodological discussion that demonstrates the importance of prioritizing Indigenous Knowledge to answer questions that may not have been considered within Western knowledge systems and shows how diverse ways of knowing can be braided to create new learnings together. “Learning together,” in practice, means recognizing that each person has knowledge and skills to contribute, which also involves shared decision making. We maintain that by “learning together,” complex problems can be understood in ways that are more meaningful and insightful than they would be if Indigenous communities, government scientists, or research consultants studied them alone.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".