Teaching Indigenous teachers: valuing diverse perspectives
Bibliographic record
Abstract
In Australia, education is failing Indigenous people, who remain the most disadvantaged group in the nation (ABS, 2007; Doyle and Hill, 2008). Indigenous students’ school participation rates are lower than their non-Indigenous peers, they leave school earlier and are less likely to complete secondary schooling (James and Devlin, 2005; Doyle and Hill, 2008). Barnhardt and Kawagley, drawing on the work of Battiste, assert that: Students in Indigenous societies around the world have, for the most part, demonstrated a distinct lack of enthusiasm for the experience of schooling in its conventional form – an aversion that is most often attributable to an alien institutional culture rather than any lack of innate intelligence, ingenuity, or problem-solving skills on the part of the students. (2005, p 10) In Australia, Indigenous students are under-represented in universities and other tertiary education institutions. Only 26% of those aged 25–64 have obtained a non-school qualification and 5% have obtained a bachelor’s degree and above. This compares unfavourably with the non-Indigenous population, where 53% have a non-school qualification and 21% have a bachelor’s degree (ABS,2008). Similar results are evident in other First Nations communities such as those in Canada (Freeman, 2008) and the US (Locke, 2004). As a means to improve Indigenous students’ participation in schooling, there have been ongoing calls for many years in Canada, North America, New Zealand and Australia to increase the number of Indigenous teachers so that students can be taught by those who best understand their needs and cultural backgrounds (Locke, 2004; Reid, 2004; White et al., 2007).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.030 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".