SES and grammatical measures (Weiler et al., 2021)
Bibliographic record
Abstract
<b>Purpose: </b>The purpose of this study was to evaluate the relative contribution of socioeconomic status (SES) on three grammatical measures—finite verb morphology composite (FVMC), percent grammatical utterances (PGU), and clausal density—in children between the ages of 4 and 9 years.<b>Method: </b>Data for this study were from the normative sample in the Edmonton Narrative Norms Instrument. For 359 children, hierarchical linear regression was performed to evaluate the amount of variance in FVMC, PGU, and clausal density that was uniquely explained by SES after accounting for child chronological age and language status (typical, impaired).<b>Results: </b>After child age and language status were controlled, SES was a significant predictor of PGU and clausal density scores, but not of FVMC scores. SES uniquely accounted for 0.5% of variance in PGU scores and 0.8% of variance in clausal density scores.<b>Conclusions: </b>Consistent with maturational accounts of children’s development of tense markers, results of this study offer evidence that, among grammatical measures, FVMC is uniquely robust to variation in SES. Although significant, the variance of PGU and clausal density scores uniquely accounted for by SES was close to minimum. Clinicians can therefore include these three grammatical measures for assessing children of different socioeconomic backgrounds.<br><b>Supplemental Material S1.</b> Mean (standard deviation) of demographic measures of children by language status and age.<br><b>Supplemental Material S2.</b> Mean (standard deviation) of grammatical measures by language status and age (<i>N</i> = 359). <br>Weiler, B., Schneider, P., & Guo, L.-Y. (2021). The contribution of socioeconomic status to children's performance on three grammatical measures in the Edmonton Narrative Norms Instrument.<i> Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2021_JSLHR-20-00576
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.238 | 0.039 |
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; both teacher heads agree on what is shown here.
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".