Gender Gaps in Indigenous Socioeconomic Outcomes: Australian Regional Comparisons and International Possibilities
Bibliographic record
Abstract
International literature clearly demonstrates the potential for gender-based inequalities to constrain development processes. In the United Nations Development Programme Gender-related Development Index, Australia ranks in the top five across 177 countries, suggesting that the loss of human development due to gender inequality is minor. However, such analysis has not been systematically applied to the Indigenous Australian population, at least in a quantitative sense. Using the 2006 Australian Census, this paper provides an analysis across three dimensions of socioeconomic disparity: Indigeneity, gender, and geography. This paper also explores the development of a similar gender-related index as a tool to enable a relative ranking of the performance of Indigenous males and females at the regional level across a set of socioeconomic outcomes. The initial findings suggest that although there is a substantial development gap between Indigenous and non-Indigenous Australians, the development loss from gender-related inequality for Indigenous Australians is relatively small. Higher life expectancy and education attainment for Indigenous females balances out their slightly lower earnings to a large extent. At the regional level, Indigenous females tend to fare better than Indigenous males for the set of indicators chosen; and, this is particularly true in capital cities.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".