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Record W3176709576 · doi:10.1044/2021_jslhr-20-00576

The Contribution of Socioeconomic Status to Children's Performance on Three Grammatical Measures in the Edmonton Narrative Norms Instrument

2021· article· en· W3176709576 on OpenAlexaffabout
Brian Weiler, Phyllis Schneider, Ling-Yu Guo

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2021
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSocioeconomic statusNarrativePsychologySocial psychologyDevelopmental psychologySociologyLinguisticsDemographyPhilosophy

Abstract

fetched live from OpenAlex

Purpose 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. Method 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). Results 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. Conclusions 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. Supplemental Material https://doi.org/10.23641/asha.14810484.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.367
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes2
Has abstractyes

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