Ways of Learning: Indigenous Approaches to Knowledge: Valid Methodologies in Education
Bibliographic record
Abstract
A friend, whom I had not seen for some time, recently asked me what I had been doing overthe last several months. I replied, ‘I have just spent the past year in the most incredible headspace.’ This elicited an excited curiosity from my friend to hear more and I began to explain. At fifty-six years of age I had made the decision to return to academic life as a student and pursue a degree in Australian Indigenous Studies. This had been suggested and encouraged by my Aboriginal sister, Jackie Huggins, and so, with herguidance I applied and was accepted to attend the University of Queensland within the Aboriginal and Torres Strait Islander Studies (ATSIS) Unit of the Arts Faculty. It was a major step for me, for although I had been presenting lectures and workshops on aspects of traditional and contemporary Native American culture in the educational and public arenas for a decade, I had not been on the student side of the lectern for 40 years. In the first few weeks of semester one the impact of my decision was almost overwhelming. I had completed secondary school in Canada, being the first person in my family to achieve that and now here I was going to university, another first in my family.
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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 | 0.097 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".