Still Invisible: Enumeration of Indigenous Peoples in Census Questionnaires Internationally
Bibliographic record
Abstract
The international attention increasingly being paid to Indigenous peoples culminated in the 2007 adoption of the United Nations Declaration on the Rights of Indigenous Peoples by the United Nations General Assembly. Nevertheless, the lack of accurate and consistent data on Indigenous peoples hinders the creation of concrete benchmarks and monitoring mechanisms for their development. Based on the most recent census questionnaires available for 231 countries and regions for which the United Nations Statistics Division collects statistics, this study identifies the proportion and geographic distribution of questionnaires that enumerated Indigenous peoples and variations in the questions used to enumerate them. The fact is that relatively few census questionnaires enumerate Indigenous peoples. Where they were enumerated, Indigenous cultures and identities were homogenized by many censuses, and classified as minorities rather than as distinct peoples. As a result, Indigenous peoples remain invisible in large areas of the globe and the United Nations, various governmental and non-governmental organizations, and Indigenous people themselves all face overwhelming challenges in their attempts to document the existence and circumstances 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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".