Genetic and environmental influences on early literacy skills across school grade contexts
Bibliographic record
Abstract
Recent research suggests that the etiology of reading achievement can differ across environmental contexts. In the US, schools are commonly assigned grades (e.g. 'A', 'B') often interpreted to indicate school quality. This study explored differences in the etiology of early literacy skills for students based on these school grades. Participants included twins drawn from the Florida Twin Project on Reading (n = 1313 pairs) aged 4 to 10 years during the 2006-07 school year. Early literacy skills were assessed with DIBELS subtests: Oral Reading Fluency (ORF), Nonsense Word Fluency (NWF), Initial Sound Fluency (ISF), Letter Naming Fluency (LNF), and Phoneme Segmentation Fluency (PSF). School grade data were retrieved from the Florida Department of Education. Multi-group analyses were conducted separately for subsamples defined by 'A' or 'non-A' schools, controlling for school-level socioeconomic status. Results indicated significant etiological differences on pre-reading skills (ISF, LNF, and PSF), but not word-level reading skills (ORF and NWF). There was a consistent trend of greater environmental influences on pre-reading skills in non-A schools, arguably representing 'poorer' environmental contexts than the A schools. Importantly, this is the case outside of resources linked with school-level SES, indicating that something about the direct environment on pre-reading skills in the non-A school context is more variable than for A schools.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".