The Development of Picture Comprehension Across Early Environments: Evidence From Urban and Rural Toddlers in Western Kenya
Bibliographic record
Abstract
Early childhood researchers frequently use learning materials and assessments involving pictures, across different cultures and contexts. However, there is variation in when and how children across cultures and contexts begin to understand and learn from pictures. While children growing up in high-income contexts often have more experience with picture books and other kinds of two-dimensional visual symbols, children growing up in low-income, rural contexts in low- and middle-income countries often have less experience with pictures and other kinds of visual symbols. The current research leverages variation in picture experience within a geographical region to investigate whether previous picture experience is related to toddlers' (1) performance on a picture-based word learning task, and (2) referential understanding, controlling for maternal education, number of toys, caregiver talk, and caregiver play. One hundred and twenty-eight toddlers in urban and rural western Kenya (n = 64 per area), who had varying amounts of picture experience, participated in a picture-based word learning task. Preregistered analyses with the entire sample showed no relation between picture experience and performance on a picture-based word learning task, or between picture experience and referential understanding. However, exploratory analyses found a positive association between picture experience and performance on the picture-based word learning task in the urban sample, but not the rural sample. We found no association between toddlers' referential understanding and picture experience, in either sample. We discuss how these results may inform the efficacy of learning materials and the validity of assessments used with children from diverse global backgrounds.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".