Crocodiles and polar bears: technology and learning in Indigenous Australian and Canadian communities
Bibliographic record
Abstract
Crocodile infisted, swollen rivers, Troop Carriers, light planes and red dirt typifY the landscape of remote tropical Northern Territory in Australia. In contrast, the remote landscape in for northwestern Ontario in Canada is characterised by rough terrain, snow and ice, sea planes and sometimes even polar bears. 1he traditional owners of the land in these two very dijferent locations foce similar issues in accessing adult learning and ongoing educational opportunities. 1his paper compares and contrasts the experiences of two groups of adult Indigenous students, one from the northern Australian tropics and one from for Northwestern Ontario, and examines the ways that technology is used to try and bridge the distance between Indigenous adult learners' goals and educational opportunities. 1he paper's major finding is that the educational gap between Indigenous and non Indigenous learners in Canada is closing, while the gap between Indigenous and non-Indigenous Australians is widening. 1his reflects in part that Indigenous adult learners in Northwestern Ontario are being better served in comparison to their counterparts in the Northern Territory of Australia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".