Addressing the Problem of Indigenous Disadvantage in Remote Areas of Developed Nations: A Plea for More Comparative Research
Bibliographic record
Abstract
\n \t\t\tIt has been well documented that Indigenous populations in developed 'post-colonial' nations (such as Australia, New Zealand, Canada, and the United States) experience disadvantage in a number of areas when compared with their non-Indigenous counterparts. Despite (or perhaps because of) a range of policy initiatives and political approaches to addressing disadvantage, there continues to be poor understandings of what 'works' and under what conditions. There is a body of literature which compares conditions, political ideas and policy initiatives across the jurisdictions, but the bases for comparison are poorly described, there is insufficient linking of research into 'ideas' with research into initiatives and their outcomes, and there is insufficient engagement of Indigenous people in the research. This paper proposes a more rigorous approach to comparative research which is based on principals of partnership with and participation of Indigenous people. We conclude that well designed participatory comparative research can not only provide new insights to old problems, but can improve Indigenous people's access to global knowledge systems. \n
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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 | 0.045 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".