Bibliographic record
Abstract
This special issue of the Journal presents the results of recent health research among the Sami. The Sami people are an example of a group of indigenous people whose traditional territory traverses modern national borders. Today Sami can be found far beyond their traditional homeland (Sápmi) in Norway, Sweden, Finland, and Russia. Sami health research has a long history, attracting an earlier generation of scientists whose main interests were focussed on racial typology, using methods that have now been largely discredited. Today researchers are interested in a diversity of topics on the health status, health determinants, and health care of Sami, as evidenced from this issue, and employing a variety of quantitative and qualitative tools. Because the Sami population is multinational, the benefit of circumpolar collaboration is undeniable. By editing this issue, Prof. Lund has brought together teams of researchers from the Nordic countries and Russia, many of whom are aspiring emergent new researchers, and showcased some of their work. As readers can see for themselves, the future of Sami health research is bright. Another encouraging development is the establishment of the Centre for Sami Health Research at the University of Tromsø, thus providing an institutional home for a concerted research program into Sami health. One could only wish that other Nordic countries will follow suit.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".