How does SES influence the brain circuitry for literacy? Modeling the association between SES, oral language, white matter integrity, and reading
Bibliographic record
Abstract
Reading is pivotal for educational and occupational success, hence, understanding the factors contributing to reading skill variation is a major educational objective. Although cognitive and neurobiological factors that influence reading are well documented, the contributions of environmental factors, such as socioeconomic status (SES), fiv to reading-related neurobiology are relatively understudied. Studies have shown that SES predicts reading and the integrity of reading-related white matter tracts; however, the direct and indirect contributions of SES to reading via white matter integrity remain undifferentiated. Further, while oral language (both phonological awareness [PA] and vocabulary) has been positively associated with both SES and reading, only a few studies have attempted to model the SES-reading association via oral language, and none of them included white matter integrity. The current study closes these gaps by using Structural Equation Modeling in a large sample of children from the Healthy Brain Network biobank, testing the (in)direct paths by which SES (parental education) influences reading through oral language and white matter integrity. Results reveal an effect of SES on reading that is indirectly affected by oral language, though not by white matter integrity. These findings reinforce the role of oral language skills as a key pathway linking SES and reading. • Socioeconomic status (SES) predicts reading. • Phonological awareness (PA) is part of the indirect path from SES to reading. • Inferior & Superior Longitudinal Fasciculi (ILF, SLF) are linked to reading and SES. • Neither ILF nor SLF are part of the indirect path from SES to reading.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".