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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".