Past Tense Production by English Second Language Learners With and Without Language Impairment
Bibliographic record
Abstract
PURPOSE: This study investigated whether past tense use could differentiate children with language impairment (LI) from their typically developing (TD) peers when English is children's second language (L2) and whether L2 children's past tense profiles followed the predictions of Bybee's (2007) usage-based network model. METHOD: A group of L2 children with LI (L2-LI) and a matched group of L2-TD peers were administered the past tense probe from the Test of Early Grammatical Impairment (Rice & Wexler, 2001) and the Peabody Picture Vocabulary Test (Dunn & Dunn, 1997). A representative input corpus provided distributional information for each verb used. Background information was obtained via parent questionnaire. RESULTS: The L2-LI group used fewer tense-marked verbs than did the L2-TD group. In both groups, vocabulary size and word frequency predicted accuracy with regular and irregular verbs. Children omitted regular past tense marking most often after alveolar stops, dropping the allomorph /Id/; L2-TD children omitted /t/ more often than /d/. Finally, first language typology predicted past tense accuracy. CONCLUSIONS: Past tense use could potentially differentiate between English L2 children with and without LI. The impact of vocabulary, frequency, and phonological factors supported the network model and indicated profile differences between L2-LI and L2-TD children.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".