Aboriginal Well-being in Four Countries: An Application of the UNDP's Human Development Index to Aboriginal Peoples in Australia, Canada, New Zealand, and the United States
Bibliographic record
Abstract
An adaptation of the UNDP’s Human Development Index is used to compare the wellbeing of Aboriginal and non-Aboriginal populations in Australia, Canada, New Zealand, and the United States between 1991 and 2001. Using Census education and income measures, and official estimates of life expectancy, we find that despite improvements in the overall well-being of Aboriginal populations, disparities between Aboriginal and non-Aboriginal people widened in some cases. Aboriginal people in most of these countries fell behind in educational attainment, compared to non-Aboriginal populations. Incomes improved over the entire period, but fell in most of these countries between 1991 and 1996. Overall, Aboriginal populations in Australia and New Zealand had lower scores than in Canada and the U.S. However, whereas the Maori scores improved considerably between 1991 and 2001, those of the Australian Aboriginal population did not. American Indians and Alaska natives had the highest overall development scores, and smallest gaps between Aboriginal and non-Aboriginal people were found in the U.S.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".