Bibliographic record
Abstract
Sarki et al. Respond Ahmed M. SarkiPhD, MPH, Alex EzehPhD, and Saverio StrangesMD, PhD, MPH, FAHA Affiliation Ahmed M. Sarki is with the School of Nursing and Midwifery, Aga Khan University, Kampala, Uganda, and the Family and Youth Health Initiative, Jigawa State, Nigeria. Alex Ezeh is with the Dornsife School of Public Health, Drexel University, Philadelphia, PA, and the School of Public Health, University of the Witwatersrand, Johannesburg, South Africa. Saverio Stranges is with the Department of Epidemiology and Biostatistics, the Department of Family Medicine, and The Africa Institute, Western University, London, Ontario, Canada, and the Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg. CopyRightCorrespondence should be sent to Ahmed M. Sarki, PhD, MPH, Assistant Professor, School of Nursing and Midwifery, Faculty of Health Sciences, Aga Khan University, 9-11 Colonel Muammar Gaddafi Rd, PO Box 8842, Kampala, Uganda (e-mail: ahmed.sarki@aku.edu). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link. CONTRIBUTORS The authors contributed equally to this letter. https://doi.org/10.2105/AJPH.2021.306384 Accepted: May 05, 2021 Published Online: August 31, 2021
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.268 | 0.129 |
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".