Innovations in Oral Health Education and Practice
Bibliographic record
Abstract
"Innovations in Oral Health Education and Practice." Journal of the California Dental Association, 44(3), pp. 165–166 Additional informationNotes on contributorsMarcia K. BrandMarcia K. Brand, PhD, is senior advisor to the Dentaquest Foundation for oral health programs and policy and served as the deputy administrator of the Health Resources and Services Administration, U.S. Department of Health and Human Services from 2009 to 2015.Conflict of Interest Disclosure: None reported.Rebecca SlifkinRebecca Slifkin, PhD, is a clinical associate professor in the department of health policy and management at the University of North Carolina, Gillings School of Global Public Health in Chapel Hill, N.C. From 2010 to 2015, she was the director of the Office of Planning, Analysis and Evaluation, Health Resources and Services Administration, U.S. Department of Health and Human Services.Conflict of Interest Disclosure: None reported
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".