Time to Count the Breastfeeding Experiences of Women With Disabilities in Health Surveillance Efforts
Bibliographic record
Abstract
Time to Count the Breastfeeding Experiences of Women With Disabilities in Health Surveillance Efforts Hilary K. Brown PhD, and Yona Lunsky PhD, CPsych Affiliation Hilary K. Brown is with the Department of Health and Society at the University of Toronto Scarborough, and the Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. Yona Lunsky is with the Azrieli Adult Neurodevelopmental Centre at the Centre for Addiction and Mental Health, and the Department of Psychiatry, University of Toronto. CopyRightCorrespondence should be sent to Hilary K. Brown, PhD, Department of Health and Society, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada, M1C 1A5 (e-mail: hk.brown@utoronto.ca). Reprints can be ordered at https://ajph.org by clicking the “Reprints” link. CONTRIBUTORS H. K. Brown undertook the conceptualization of the commentary and drafting of the article. Y. Lunsky contributed to the conceptualization of the commentary and critically revised the article for important intellectual content. Both authors approve the final version to be published and agree to be accountable for all aspects of the work. https://doi.org/10.2105/AJPH.2023.307514 Accepted: October 23, 2023 Published Online: December 13, 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 | 0.018 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".