In Quest of Indigeneity, Quality, and Credibility in Aboriginal Post-Secondary Education in Canada: Problematic, Contexts, and Potential Ways Forward.
Bibliographic record
Abstract
Learning involves conceptual frameworks embedded in worldviews and values. The overarching problematic of Aboriginal post-secondary education is complex and multifaceted. Normative and institutional forces as well as the credentialing and certification agenda of post-secondary education limit the degree to which Aboriginal education at any level can simply go its own way. To what degree and in what ways should Aboriginal post-secondary education differ from mainstream post-secondary education — and can it? The parity paradox (Paquette & Fallon, 2010, p. xii) prevails in post-secondary as in lower-level education. Education that purports to be meaningfully “ Aboriginal ” must fulfill two seemingly opposing purposes: provide education that is grounded in Aboriginal cultures but also provide a reasonable degree of parity with the content and quality of mainstream education. In short, Aboriginal post-secondary education is situated at the nexus of colliding epistemic universes of hugely unequal power. What can and should be Aboriginal in Aboriginal post-secondary education? What is the Canadian experience to date in that respect — with particular focus on the British Columbia case example — and what can be learned from it?
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.056 | 0.051 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".