Establishing performance standards for child development: learnings from the ECDI2030
Bibliographic record
Abstract
Abstract Background Standards of early childhood development (ECD) are needed to determine whether children living in different contexts are developmentally on track. The Early Childhood Development Index 2030 (ECDI2030) is a population-level measure intended to be used in household surveys to collect globally comparable data on one of the indicators chosen to monitor progress toward target 4.2 of the Sustainable Development Goals: The proportion of children aged 24–59 months who are developmentally on track in health, learning and psychosocial well-being. Methods To define performance cut-scores for the ECDI2030 we followed a criterion-referenced standard setting exercise using the modified Angoff method. The exercise gauged the expectations from 15 global experts in ECD and was informed by representative population data collected in Mexico and the State of Palestine. The final calibrated age-specific performance cut-scores were applied to these data to estimate the proportion of children developmentally on track, disaggregated by background characteristics, including the child's sex and attendance to early childhood education. Results Through a process of standard setting, we generated robust performance standards for the ECDI2030 by establishing five age-specific cut-scores to identify children as developmentally on track. Conclusions This paper demonstrated how the standard setting methodology, typically applied to measures in the health and education fields, could be applied to a measure of child development. By creating robust criterion-referenced standards, we have been able to ensure that the cut-scores related to age for the ECDI2030 are based on performance standards set by global experts in the ECD field for defining on and off track development.
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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 | 0.172 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".