Influence des langues premières sur l’acquisition du français L3 : une étude exploratoire de l’association entre la dominance langagière et le positionnement adjectival chez les enfants
Bibliographic record
Abstract
The phenomenon of language transfer involves the replication of linguistic structures from a pre-acquired language onto a target language. In the context of L2 acquisition, the only source of transfer is the L1. However, in L3 acquisition, both L1 and L2 can influence L3. An understudied mechanism possibly involved in determining L3 transfer source is language dominance. This article presents a preliminary study testing language dominance in two L1s (English and Egyptian Arabic) to see if it could explain the source of transfer to beginner-level L3 (French). An influence of dominance on the source of transfer to an L3 was predicted. Unlike other studies, in this study, language dominance was tested by more than one measure and using simultaneous bilinguals. Language dominance was tested by measures of morphosyntax and vocabulary obtained from oral production tasks and by a background questionnaire providing information on the input (language exposure) and output (language use) of each pre-acquired language. The source of transfer was measured by a preference task testing concrete and evaluative adjective placement in 21 children aged 7 to 10 years old. The preliminary results of this study suggest that language dominance of pre-acquired languages does not play a role regarding the source of transfer to L3, which is in agreement with some previous studies (e.g. Lloyd-Smith et al., 2021; Ramos Feijoo & García Mayo, 2023).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".