A path to reconciliation between Indigenous and settler–colonial epistemologies
Bibliographic record
Abstract
There is a movement across settler–colonial institutions of education and research to engage with Indigenous Peoples and Knowledges. Many settler and Indigenous governments are pursuing pathways to move forward together to address global problems such as climate change. However, given the pervasive history of exploitation and displacement of Indigenous communities, this development has caused some concern among Indigenous leaders and scholars. At the 2022 Annual Meeting of the Ecological Society of America (ESA) in Montreal, Canada, the Traditional Ecological Knowledge Section of the ESA hosted a 2‐day workshop. This gathering of 21 Indigenous environmental scientists included scholars from across the career and professional spectrum. By consensus, workshop participants identified three emergent themes—Engage, Heal, and Reconcile—that provide a pathway toward reconciliation between Indigenous and settler–colonial ways of knowing. This path allows for an ever‐greater sharing of institutional resources and power toward a co‐equal interfacing of Indigenous Knowledges and settler science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| 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".