French-English bilingual children’s sensitivity to child-level and language-level input factors in morphosyntactic acquisition
Bibliographic record
Abstract
This chapter presents three studies from a research program investigating how French-English bilingual children’s morphosyntactic acquisition is influenced by child-level input factors such as bilingual versus monolingual learning, and variation in home input among bilinguals, as well as language-level input factors such as the token/type frequency and distributional consistency of morphosyntactic constructions. Two existing studies from this program found sensitivity to these input factors in 4-year-olds’ acquisition of past tense morphology in both French and English (Paradis, Nicoladis, Crago, & Genesee 2011) and in 6-year-olds’ acquisition of bound and free verb morphology in English (Paradis 2010). A new study reported in this chapter examined the French morphosyntax of bilingual 6-year-olds as compared to 11-year-old bilingual children and 6-year-old monolingual French-speaking children. Children were given both elicitation and grammaticality judgement tasks probing their abilities with French direct object clitics and a control structure, definite articles. Similar to the previous studies, differences between monolinguals and bilinguals, and among bilinguals, varied according to home input factors and morphosyntactic construction; however, most differences were neutralized in the older bilingual group. The final section discusses results from all three studies that point to the combined influence of multiple sources of input variation on bilingual morphosyntactic acquisition.
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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 | 0.002 |
| 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 | 0.001 |
| 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 | 0.000 |
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".