Early emergence of structural constraints on code-mixing: evidence from French–English bilingual children
Bibliographic record
Abstract
Does young bilingual children's code-mixing obey the same structural constraints as bilingual adults' code-mixing? The present study addresses this question using code-mixing data from 15 French–English bilingual children filmed in conversation with both parents at six-month intervals from the age of 2;0 to 3;6. The children's code-mixed utterances were examined for violations of the principles set out in the Matrix-Language Frame model (e.g. Myers-Scotton, 1993, 1997). The results show that the children obeyed all the constraints set out in the Matrix Language Frame model the majority of the time. With respect to the Morpheme Order Principle and to the interaction of Congruence and Matrix Language Blocking, they demonstrated consistent adherence with only marginal violations from the outset. In contrast, the children produced comparatively more frequent violations of the System Morpheme Principle and showed increasing adherence to this principle over time. We discuss possible explanations for the contrast between the children's performance on the System Morpheme Principle and the other constraints, which include the unequal emergence of INFL in the acquisition of French and English.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".