Cultural Strength: Restoring the Place of Indigenous Knowledge in Practice and Policy
Bibliographic record
Abstract
Indigenous traditions, cultures and identities are not historical artefacts or museum pieces; they are vital and contemporary, and they are critical to indigenous wellbeing and our shared understanding of how to live in the world. Importantly, approaches based on indigenous cultural strength must drive engagement with the environment, lead settlements between indigenous peoples and governments, drive new approaches to education and health care, and shape the direction of academic research and public policy. At a symposium held at AIATSIS in February this year, Professor Taiaiake Alfred spoke about indigenous resurgence in Canada, in particular the experiences of his Kahnawa:ke Mohawk community. This article is an edited version of Professor Alfred's address.
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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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.054 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".