Creating Indigenous Spaces in the Academy: Fulfilling our Responsibility to Future Generations
Bibliographic record
Abstract
An innovative scholarship led by indigenous peoples is emerging worldwide with an emphasis on questioning the knowledge, privileges and paradigms of the Western academy. One of the challenges of supporting new indigenous scholarship within the Western academy is to find ways to engage meaningfully with indigenous knowledge. The Native Studies PhD programme at Trent University, Ontario, Canada, has designed the Bimaadiziwin/Atonhetseri:io option to provide students at an advanced level of study with an opportunity to apprentice with elders and indigenous knowledge holders. This paper reports on the experiences of the programme, its conceptual design and evolution, and reflections of elders, students and administrators who have been involved with different aspects of the programme. Students report deeply transformative journeys in working with elders who transmit indigenous knowledge. At the same time, tensions surface as the PhD programme mediates these experiences in terms that are recognisable to the Western academy.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.034 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".