Centering Indigenous Knowledges in ecology and beyond
Bibliographic record
Abstract
There is a resurgent enthusiasm for Indigenous Knowledges (IK) across settler–colonial institutions of research, education, and conservation. But like fitting a square peg in a round hole, IK are being forced into colonial systems, and then only as marginal alternatives. To address this mismatch, the Traditional Ecological Knowledge Section of the Ecological Society of America (ESA) hosted a 2‐day workshop—entitled Elevating Indigenous Knowledges in Ecology—at the 2022 ESA Annual Meeting, which was held on Kanien'keháka (Mohawk) and Ho‐de‐no‐sau‐nee‐ga (Haudenosaunee) territories in Montreal, Canada. This gathering of 21 interdisciplinary Indigenous ecologists included scholars from across the career and professional spectrum. By consensus, workshop participants (including the authors of this article) identified four emergent themes and respective guiding questions as a pathway toward the transformation of settler–colonial institutions into IK‐led spaces. We highlight this pathway to support actions toward systemic change, inspire future directions for Indigenous and non‐Indigenous ecologists, and nurture stronger relationships between Indigenous communities and the Western sciences, toward actualized decoloniality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".