Indigenous Peoples and Cultural Safety in Public Health
Bibliographic record
Abstract
Indigenous Peoples and Cultural Safety in Public Health Megan Carlson MPH, and Nicole Redvers ND, MPH Affiliation Megan Carlson is with the Department of Population Health, School of Medicine and Health Sciences, University of North Dakota, Grand Forks. Nicole Redvers is with the Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada. CopyRightCorrespondence should be sent to Megan Carlson, Department of Population Health, School of Medicine and Health Sciences, University of North Dakota, 1301 N Columbia Rd, Stop 9037, Grand Forks, ND, 58202 (e-mail: megancarlson.ak@gmail.com). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link. CONTRIBUTORS M. Carlson and N. Redvers performed data curation, wrote the original draft, and reviewed and edited the article. M. Carlson was responsible for conceptualization and methodology. https://doi.org/10.2105/AJPH.2023.307363 Accepted: June 06, 2023 Published Online: September 06, 2023
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".