Sources of individual differences in the acquisition of tense inflection by English second language learners with and without specific language impairment
Bibliographic record
Abstract
ABSTRACT The goal of this study was to investigate whether individual difference factors influence the second language (L2) learning of children with specific language impairment (SLI) and children with typical development (TD) differently. The study focuses on tense inflection development in English L2 children. The roles of age of L2 acquisition, length of L2 exposure, and first language (L1) were examined. Twenty-four pairs of 4- and 5-year-old English L2 children with SLI and English L2 children with TD participated in the study. Children's responses on the third person singular and regular past tense probes of the Test of Early Grammatical Impairment (Rice & Wexler, 2001) were analyzed using logistic mixed regression modeling and classification procedures. For all children, those who started learning English later performed better than children who started learning English earlier, but the advantage of an older age of acquisition was particularly present in the L2 with SLI group. For children in the L2 group with TD, their accuracy with tense inflection clearly increased with longer L2 exposure, but this was not found for the L2 children with SLI. Finally, L2 children with TD were better able to transfer L1 knowledge than L2 children with SLI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".