Indigenous Knowledges: A Strategy for First Nations Peoples Engagement in Higher Education
Bibliographic record
Abstract
This chapter will consider the position of First Peoples' engagement with higher education from the perspective of the embracing of Indigenous knowledge. This necessarily involves the taking of a wider view, to look through the lens of the administration of justice, and in so doing to attempt to develop more sophisticated and effective practices of inclusion. The authors argue for improving the methodological approach of including Indigenous knowledge so as to more effectively resolve matters that come before the law, as well as addressing historic and ongoing colonial injustice. They will explore methodologies for social inclusion within the legal order, framed within the context of inclusion in higher education. Critiques have led to programs for inclusion ofIndigenous knowledges and experience. Similarly, commitments to social justice have led to acceptance of the need for reform to formal law, administration and education. However, beyond inclusion of First Peoples! in governance projects, there has been no attention to developing appropriate methodology. This oversight has meant Indigenous knowledges are misrepresented or co-opted even while being included. Judith Butler asks: 'How do we understand those sets of conditions and dispositions that account for the "state we are in" (which could, after all, be a state of mind) from the "state" we are in when and if we hold rights of citizenship or when the state functions as the provisional domicile for our work?' For First Peoples, these questions have a particular theoretical resonance and practical implication. How do First Peoples express and retain an Indigenous identity within the state? Many First Peoples assert that we are subjects in international law, while the state asserts we are their Indigenous Peoples and exist within the domestic paradigm of the state.
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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 | 0.032 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.035 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".