“We don’t have things for counting”: An exploration of early numeracy skills and home learning experiences of children growing up in poverty in South Africa
Bibliographic record
Abstract
A child’s home environment has been shown to be related to the development of early numeracy skills in some countries. However, significant relationships between home learning environment and math achievement have not consistently been found, and likely vary across different cultural and socio-political contexts. Here we explored the home environment and early numeracy skills of 243 children (3-5 years), who were not attending preschool programmes in very low-income settings in Cape Town, South Africa. Caregivers completed a questionnaire including information regarding experiences of children in the home; children completed a number identification task, a counting task and the Give-N task. The amount of resources in the home learning environment (e.g. the number of books and toys), frequency of home learning activities caregivers did with their children, and caregiver levels of education and income were not associated with number knowledge. While the home learning environment has been shown to be important for developing early numeracy skills in previous research, this study suggests that factors other than the home learning environment may also be important targets to foster numeracy skills and school readiness in low-income settings in South Africa.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".