The Effect of Age of First Exposure on Vocabulary, Mean Length of Utterance, Morphosyntactic Accuracy, and Semantic and Sentence-Level Patterns in the First 2 Years of French Second-Language Learning by Preschool- to Adolescent-Age Mandarin Speakers
Bibliographic record
Abstract
BACKGROUND: Bilingual assessment is particularly difficult in the very first period of children's second language (L2) exposure. This exploratory, longitudinal study examined L2 learning after 1 and 2 years of L2 exposure by young immigrants and how it is affected by their age at first exposure to the L2 (AoE). METHOD: Participants were 18 immigrants ranging in age from 2;11 to 14;2 (years;months), all within their first year in Montreal at Time 1, enrolled in a French school or day care, and from a Mandarin first language background. Participants were tested again a year later. Measures included receptive and expressive French vocabulary tests and conversational language samples analyzed using traditional measures of mean length of utterance (MLU) and morphological accuracy as well as novel measures of semantic and sentence-level patterns. RESULTS: Performance was relatively high already at Time 1 and increased significantly at Time 2 in both vocabulary and MLU. At Time 2, vocabulary scores were below normative values, whereas MLU was within expected values relative to monolingual and simultaneous bilinguals for the majority of the participants. However, higher MLUs were accompanied by more instances of both semantic errors and creative semantic strategies. French performance was strongly related to AoE; with amount of exposure equivalent, older participants outperformed the younger ones on MLU and vocabulary. Semantic errors and creative uses were strongly predicted by AoE; however, morphological accuracy and number of agrammatical utterances were not. CONCLUSIONS: This initial period of French learning involved a rapid growth spurt for most of the participants. We argue that the pattern observed, particularly among the older children, constitutes an early stage of L2 learning characterized by long utterances that are also frequently hard to understand as speakers encounter challenges and use creative strategies in their attempt to convey meaning. Comparison with normative reference bases for monolinguals and bilinguals with greater cumulative L2 exposure who have similar MLUs should be done with much caution during this early period.
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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 | 0.001 | 0.001 |
| 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".