The Home Literacy Environment and Reading Development of Children With and Without Learning Disabilities
Bibliographic record
Abstract
Home literacy environment (HLE) refers to children's exposure to and engagement in reading-related activities in the home. Although HLE is known to be related to successful early reading achievement in general, less is known about this relation for students with learning disabilities (LDs). We investigated the relation between HLE and reading achievement using a sample of 2090 children from the ECLS-K:2011 dataset, half of whom were identified as students with LDs and half serving as controls. Latent growth curve modeling was used to examine growth in reading from kindergarten (age 5) through fifth grade (age 10). For both groups, growth was characterized by mastery learning with a negative correlation between intercept (i.e., performance at the first time point) and slope (rate of growth). Compared to controls, LD children had a lower mean intercept but a higher mean slope. HLE was positively related to intercept for both groups. However, the positive relation between HLE and reading did not extend to later grades, with a small but significant negative relation between HLE and slope for both groups that was a byproduct of the negative correlation between intercept and slope. The pattern of results remained the same after controlling for socioeconomic status (SES). It appears HLE is equally important to the reading achievement of both groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".