Are Second Language Learners Just as Good at Verb Morphology as First Language Learners
Bibliographic record
Abstract
We addressed whether children learning French as a first (L1) and multilingual children (MUL, for whom French is a second or third language) are sensitive to sub-regular verb conjugation patterns (i.e., neither default, nor idiosyncratic) (e.g., Albright, 2002; Clahsen, 1999). Some argue that children with other first languages have more difficulty learning verb conjugation patterns due to their lesser exposure to the language (e.g., Nicoladis, Palmer, & Marentette, 2007). We hypothesized that older children would perform better than younger children and that L1 and MUL children learning French would process verb inflection patterns differently based on their default status (-er verbs), and reliability (e.g., sub-regular-ir verbs), with MUL children showing weaknesses in non-default types (Royle, Beritognolo, & Bergeron, 2012). We elicited verbs in 169 children (aged 67 to 92 months) attending preschool (n = 105) or first grade (n = 64), who were L1 or MUL learners of Québec French, using 24 verbs with regular, sub-regular, and irregular participle forms (6 of each, ending in /e/, /i/, /y / or IDiosyncratic) in the passé composé (perfect past). Using our Android application Jeu de verbes, verbs were presented with images (see Figure 1) to each child in an infinitival form (infinitival complements or the periphrastic future, e.g., Marie va cacher ses poupées ‘Mary will hide her dolls’) and present tense contexts (e.g., Marie cache toujours ses poupées ‘Mary always hides her dolls’). Children were prompted to produce the passé composé by answering the question ‘What did she do yesterday, Marie?’.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".