A Framework for Doing Things <i>in a Good Way</i>: Insights on Mshiikenh (Freshwater Turtle) Conservation Through Weaving Western Science and Indigenous Knowledge in Whitefish River First Nation
Bibliographic record
Abstract
Co-developed conservation programs for Species At-Risk, created in partnership between Indigenous Nations and non-Indigenous researchers, represent a vital shift toward effective species recovery strategies that are culturally respectful and contribute to reconciliation within the natural sciences. By weaving together diverse knowledge systems and prioritizing Indigenous laws, knowledge values, and community priorities, these collaborations aim to restore species at-risk populations and prevent species extirpation-a task of increasing urgency amid the global biodiversity decline. As similar partnerships gain momentum across Canada, it is critical to reflect on approaches that honor Indigenous perspectives and actively avoid the historical harms associated with colonial research practices on Indigenous lands. This paper draws on insights from a community-driven species at-risk conservation initiative at Whitefish River First Nation, or Wiigwaaskingaa (Elder Arthur McGregor baa, 2000), in Northern Mnidoo Gamii (Georgian Bay), Ontario, Canada, where community members and researchers co-developed a mshiikenh (freshwater turtle) conservation project. We present this paper as a chance to reflect on our iterative collaborative process, its challenges, successes, and key lessons learned. We focus on six key themes for meaningful collaboration: co-developing project objectives, honoring community priorities, respecting data sovereignty, the journey of learning and unlearning, focusing on a community-guided trajectory, and promoting tangible outcomes. By highlighting specific examples from Whitefish River First Nation's mshiikenh conservation project, we demonstrate the value of community-engaged research as a pathway forward for Species At-Risk conservation in Canada and beyond.
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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 | 0.024 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.045 | 0.096 |
| Scholarly communication | 0.026 | 0.017 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.008 | 0.014 |
| 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".