Unpacking the Effects of Parents on Their Children’s Emergent Literacy Skills and Word Reading: Evidence from Urban and Rural Settings in China
Bibliographic record
Abstract
Purpose We examined the role of distal (parents’ education, family’s income, parents’ expectations, and parents’ attitudes to the home teaching of literacy) and proximal (formal and informal home literacy environment, access to literacy resources, and extracurricular activities) parental factors in children’s early literacy skills and whether the relations vary across affluent and disadvantaged societies in China.Method Five hundred fifty-three third-year kindergarten Chinese children (Mage = 74.59 months) were recruited from Jining, Luqiao, and Mapo and were assessed on measures of phonological awareness, vocabulary, pinyin knowledge, and word reading. Their parents filled out a questionnaire on their education and income as well as on the frequency of engaging in different home literacy activities, their expectations and attitudes to the home teaching of literacy, and their children’s extracurricular activities.Results Results of multigroup analyses and mediation analyses revealed both direct and indirect effects of both distal and proximal parental factors on emergent literacy skills and word reading. In addition, the models were strikingly similar across the two settings.Conclusion The findings suggest that the pathways of differential influences from parental factors to children’s early literacy skills may be similar across socioeconomic contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".