Correction: Feasibility and acceptability of implementing the Global Scales for Early Development (GSED) package for children 0–3 years across three countries
Bibliographic record
Abstract
Following publication of the original article [1], the authors reported an error in the author names of Magdalena Janus, Yvonne Schönbeck, Abdullah H. Baqui and Rasheda Khanam, and an error to the affiliations of authors Tarun Dua, Romuald Kouadio E. Anago, Michelle Perez Maillard and Gillian Lancaster. The incorrect author name is: Magdalana Janus. The correct author name is: Magdalena Janus. The incorrect author name is: Yvonne Schonbeck. The correct author name is: Yvonne Schönbeck. The incorrect author name is: Abdullah Baqui. The correct author name is: Abdullah H. Baqui. The incorrect author name is: Rasheda Khanum. The correct author name is: Rasheda Khanam. Authors Tarun Dua and Michelle Perez Maillard should be affiliated to “Department of Mental Health and Substance Use, World Health Organization, Geneva, Switzerland”, author Romuald Kouadio E. Anago should be affiliated to “Innovations for Poverty Action, IPA Côte d’Ivoire, Abidjan, Côte d’Ivoire”, and author Gillian Lancaster should be affiliated to “School of Medicine, Keele University, Keele, UK”. The author group has been updated above and the original article [1] has been corrected.
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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 | 0.012 | 0.319 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.058 | 0.022 |
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".