Measuring indigenous populations across nations: Challenges for methodological alignment
Bibliographic record
Abstract
The social and political importance of the world's Indigenous peoples is highlighted by the United Nations and by a range of National Statistical Organisations and government agencies internationally who aim to identify and address some of the distinct social and economic characteristics observed in Indigenous populations. This paper outlines the historical and social context around enumeration and measurement of Indigenous peoples in Australia and offers an outline of current operational approaches across administrative and survey data. It also gives a comparative account of approaches taken by the United States of America, Canada and New Zealand, discussing historical contexts, their notions of Indigeneity and the collection methodology employed. Considerations are then offered toward the development of an internationally consistent approach to the measurement of Indigenous peoples. While Indigenous data is collected and compared across nations, collection methodologies differ, making comparisons less reliable and giving rise to the consideration for a standard international recording methodology. This preliminary review of current approaches and the documentation of known collection issues are of value in encouraging a wider strategic discussion around approaches to Indigenous statistics amongst nations.
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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 | 0.509 | 0.629 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".