Predictive Strength of Auditory Maturity Across Different Levels of Language Ability: An Exploratory Quantile Regression Study
Bibliographic record
Abstract
Auditory evoked potential-age (AEP-age) is proposed to index auditory maturation and has been found to predict language skills in children with and without a language disorder. However, reporting average effects using linear regression does not fully capitalize on the potential of AEP-age to estimate individual differences in young children. This study used a quantile regression approach to examine the predictive utility of AEP-age for 105 typical and neurodiverse 7-10-year-old children (61 males; 44 females; largely monolingual English) with varying language skills without creating subgroups. Although linear regression did not find an association between AEP-age and language skills, the quantile model added specificity by revealing differential associations. AEP-age was only related to language skills for children at around the median point of the language continuum, but, not for those at the lowest or highest end of the language distribution. Overall, the quantile regression methodology provides us with the flexibility of understanding how AEP-age is related to different language abilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".