Bibliographic record
Abstract
Full list of author information is available at the end of the articleIntroduction Health inequalities continue to be one of the critical chal-lenges of our times. Reducing health inequalities as well as improving overall health are now considered as twin goals of population health in many countries and sub-national jurisdictions [1]. Globally, increasing inequality is identified as one of the five shifts of contemporary issues since 2000, when the Millennium Development Goals (MDGs) were set for 2000–2015, and considerations for inequalities in general and in health care and health domains are promin-ent in efforts to identify post-MDGs, Sustainable Develop-ment Goals for 2015–2030 [2]. Regular reporting of health inequalities is essential to monitoring progress of, or lack thereof, efforts to reduce * Correspondence: yukiko.asada@dal.ca1Department of Community Health and Epidemiology, Dalhousie University,Yukiko Asada1*, Alyce Whipp1, David Kindig2, Beverly Billard1 and Barbara Rudolph3
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.765 | 0.572 |
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".