Are South African children on track for early learning? Findings from the South African Thrive By Five Index 2021 Survey
Bibliographic record
Abstract
Abstract We report on a national South African multistage cluster sampling survey of early development in 5,222 children aged 50-59 months enrolled in preschool programmes. Children were assessed on the Early Learning Outcomes Measure (ELOM 4&5), the ELOM Social-Emotional Rating Scale, and linear growth (height-for-age), in the last quarter of 2021. ELOM 4&5 is standardised for South Africa and measures development in five domains: Gross Motor, and Fine Motor Development, Numeracy and Mathematics, Cognition and Executive Functioning, and Literacy and Language skills. Cut scores are used to classify children as On Track, Falling Behind, or Falling Far Behind expected developmental standards. Post-survey weights were computed, permitting us to interpret results as representative of children attending early learning programmes. Only 45.7% of the sample were On Track overall. Apart from Literacy and Language (54.7%), no other domain exceeded 50% On Track. Children who were better off socio-economically achieved higher scores (except for Gross Motor Development). Height-for-age measurements revealed a stunting rate of 5.1%, (>8.8% in one province). A mixed linear model analysis showed that age, sex, quintile, growth status, and socio-emotional score were significant predictors of the total ELOM 4&5 score, with growth status and quintile being stronger predictors. The results indicate concerningly poor preparedness for school. Two further surveys prior to 2030 will be undertaken and used to establish the country’s progress toward meeting Sustainable Development Goal 4.2: “all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary education”.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".