“Awakening the sleeping giant”: re-Indigenization principles for transforming biodiversity conservation in Canada and beyond
Bibliographic record
Abstract
Precipitous declines in biodiversity threaten planetary boundaries, requiring transformative changes to conservation. Colonial systems have decimated species and ecosystems and dispossessed Indigenous Peoples of their rights, territories, and livelihoods. Despite these challenges, Indigenous-governed lands retain a large proportion of biodiversity-rich landscapes. Indigenous Peoples have stewarded the land in ways that support people and nature in respectful relationship. Biodiversity conservation and resurgence of Indigenous autonomies are mutually compatible aims. To work towards these aims requires significant transformation in conservation and re-Indigenization. Key to both are systems that value people and nature in all their diversity and relationships. This paper introduces Indigenous principles for re-Indigenizing conservation: ( i) embracing Indigenous worldviews of ecologies and M’sɨt No’kmaq, ( ii) learning from Indigenous languages of the land, ( iii) Natural laws and Netukulimk, ( iv) correct relationships, ( v) total reflection and truth, ( vi) Etuaptmumk—“two-eyed seeing,” and “strong like two people”, and ( vii) “story-telling/story-listening”. Although the principles derive primarily from a Mi’kmaw worldview, many are common to diverse Indigenous ways of knowing. Achieving the massive effort required for biodiversity conservation in Canada will entail transformations in worldviews and ways of thinking and bold, proactive actions, not solely as means but as ongoing imperatives.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.045 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".