Co-creating Ethical Space in wildlife conservation: a case study of moose (Mooz; <i>Alces alces</i>) research and monitoring in the Robinson Huron Treaty region (Ontario, Canada)
Bibliographic record
Abstract
The strengths of Indigenous Knowledges and need for reconciliation are increasingly recognized within conservation, leading to a rise in collaborative, cross-cultural research initiatives. As both a cultural keystone and important harvest species, moose are of value to both Indigenous and non-Indigenous Peoples, presenting an opportunity to pursue moose monitoring strategies that embrace the strengths of Indigenous and Western knowledges. While various frameworks provide theoretical direction on how to do so, few resources outline how to apply them in practice. Leaning on guidance of the Ethical Space framework, we explored the meaning and application of value-based approaches in the context of moose monitoring in central Ontario through semi-structured interviews with First Nation communities, the Ontario provincial crown government, and academic researchers. Collectively, 20 core values were identified to be important when bringing Indigenous and non-Indigenous partners together, coupled with a range of tangible actions necessary for fostering Ethical Space. Values and actions reflected three main themes: an emphasis on the long term, the importance of building and maintaining relationships, and the ability to evolve and adapt over time. Insights from this research provide tools and guidance for others interested in enacting Ethical Space in the context of cross-cultural wildlife monitoring and research.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.026 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".