Correction: Rating early child development outcome measurement tools for routine health programme use
Bibliographic record
Abstract
The authors would like to correct the funding statement. Currently it reads as 'Funding: This supplement has been made possible by funding support from the Bernard van Leer Foundation. Saving Brains impact and process evaluation funded by Grand Challenges Canada.' This funding statement is correct for all the other papers in the series but for this specific paper a key funder (Children's Investment Fund Foundation) was not listed, and Grand Challenges Canada should not be listed. The correct funding statement should be as follows: 'Funding: This supplement has been made possible by funding support from the Bernard van Leer Foundation (funding costs of publication and launch) and the Children's Investment Foundation Fund (partial funding of author time (DB) and (JC) and funding for EN-SMILING study).
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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 | 0.272 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.051 | 0.028 |
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".