Understanding Indigenous knowledge of conservation and stewardship before implementing co-production with Western methodologies in resource management: A focus on fisheries and aquatic ecosystems
Bibliographic record
Abstract
In the face of an increasing global human population and multiple anthropogenic environmental stressors including climate change, the limitations of relying solely on Western science and approaches to mitigating impacts, conserving biodiversity, and managing resources sustainably is apparent. Many Indigenous Peoples have lived sustainably as part of their respective environments for millennia, passing conservation and management practices down generations despite colonization and genocide. Long-standing Indigenous knowledge and philosophies offer alternate worldviews that can complement Western conservation and resource management and may strengthen efforts to restore environmental integrity and conserve species and ecosystems. Researchers often tout the co-production of knowledge with Indigenous collaborators using frameworks like the Kaswentha (Two Row Wampum—Haudenosaunee) and the Etuaptmumk (Two Eyed Seeing—Mi’kmaw) without first seeking to understand the foundations of Indigenous knowledge itself, and its deep roots in environmental sustainability. We develop a thesis of the embedded relational nature of Indigenous knowledges and the unique strengths and perspectives that must be understood before effective and ethical co-production can be possible. We contend that Indigenous knowledge must be treated as a distinct framework to inform conservation and stewardship of biodiversity and nature, rather than selectively integrating it into Western science. Building relationships with local Indigenous nations will help actualize sustainable practices that are rooted in millennia of empirical data. This will help to promote a shift toward a holistic and relational worldview for more impactful conservation action.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| 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".