Intergenerational Effects on Children’s Reading Comprehension in Chinese: Evidence from a 3-Year Longitudinal Study
Bibliographic record
Abstract
Purpose We examined whether the relations between home literacy environment (HLE), children’s independent reading, and emergent literacy and reading skills are confounded by parents’ reading skills (a genetic proxy).Method One hundred eighty-nine Chinese children (Mage = 74.26 months, 40% female) were followed from kindergarten to Grade 2 and were assessed on emergent literacy skills (pinyin knowledge, phonological awareness, and vocabulary) and word reading in kindergarten and on reading comprehension in Grade 2. Their parents (both mothers and fathers) were also assessed on reading (word reading and reading comprehension) and completed questionnaires on HLE (code-related activities, meaning-related activities, and access to literacy resources) in kindergarten and their children’s independent reading in Grade 2.Results Results of structural equation modeling showed that access to literacy resources was associated with children’s vocabulary, and code-related activities indirectly predicted reading comprehension through pinyin knowledge after controlling for parents’ reading skills. Parents’ reading skills indirectly predicted children’s reading comprehension through children’s independent reading.Conclusion Code-related activities and access to literacy resources may have a true effect on children’s reading development over and above the effect of parents’ reading skills as a proxy for genetic transmission. The children of parents with better reading skills may engage in more independent reading, which may be related to their better reading comprehension.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".