Towards estimating the indigenous population in circumpolar regions
Bibliographic record
Abstract
Despite the importance of indigenous people in the Arctic, there is no accurate estimate of their size and distribution. We defined indigenous people as those groups represented by the "permanent participants" of the Arctic Council. The census in Canada, Russia and the United States records status as an indigenous person. In Greenland, a proxy measure is place of birth supplemented by other information. For the Nordic countries we utilized a variety of sources including registered voters' lists of the various Sami parliaments and research studies that established Sami cohorts. Overall, we estimated that there were about 1.13 million indigenous people in the northern regions of the 8 Member States of the Arctic Council. There were 8,100 Aleuts in Alaska and the Russian North; 32,400 Athabaskans in Alaska and northern Canada; 145,900 Inuit in Alaska, northern Canada and Greenland; 76,300 Sami in northern Norway, Sweden, Finland and Russia; and 866,400 people in northern Russia belonging to other indigenous groups. Different degrees and types of methodological problems are associated with estimates from different regions. Our study highlights the complexity and difficulty of the task and the considerable gaps in knowledge. We hope to spur discussion of this important issue which could ultimately affect strategies to improve the health of circumpolar 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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".