Indigenous well-being in four countries: An application of the UNDP'S Human Development Index to Indigenous Peoples in Australia, Canada, New Zealand, and the United States
Bibliographic record
Abstract
BACKGROUND: Canada, the United States, Australia, and New Zealand consistently place near the top of the United Nations Development Programme's Human Development Index (HDI) rankings, yet all have minority Indigenous populations with much poorer health and social conditions than non-Indigenous peoples. It is unclear just how the socioeconomic and health status of Indigenous peoples in these countries has changed in recent decades, and it remains generally unknown whether the overall conditions of Indigenous peoples are improving and whether the gaps between Indigenous peoples and other citizens have indeed narrowed. There is unsettling evidence that they may not have. It was the purpose of this study to determine how these gaps have narrowed or widened during the decade 1990 to 2000. METHODS: Census data and life expectancy estimates from government sources were used to adapt the Human Development Index (HDI) to examine how the broad social, economic, and health status of Indigenous populations in these countries have changed since 1990. Three indices - life expectancy, educational attainment, and income - were combined into a single HDI measure. RESULTS: Between 1990 and 2000, the HDI scores of Indigenous peoples in North America and New Zealand improved at a faster rate than the general populations, closing the gap in human development. In Australia, the HDI scores of Indigenous peoples decreased while the general populations improved, widening the gap in human development. While these countries are considered to have high human development according to the UNDP, the Indigenous populations that reside within them have only medium levels of human development. CONCLUSION: The inconsistent progress in the health and well-being of Indigenous populations over time, and relative to non-Indigenous populations, points to the need for further efforts to improve the social, economic, and physical health of Indigenous peoples.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".