The Modeling Hypothesis and child bilingual codemixing
Bibliographic record
Abstract
According to one explanation of child bilingual codemixing(the modeling hypothesis), bilingual children's rates of mixing are related to rates of mixing in the input addressed to them. An assumption of this hypothesisis that bilingual children are sensitive to codemixing in the input and that they can adjust their own rates on-line in accordance with the input. Despite its widespread appeal, evidence concerning its validity has been largely inconclusive. The assumption is largely noncontro versial in the case of older bilingual children, as evidenced by their adoption of the patterns of codemixing of the speech communities in which they live. However, it is not clear whether young bilingual children have the cognitive and linguistic capacities implicated by this assumption. The present study sought to examine this assumption directly. Six French-English bilingual children(average age 2;4 years) were recorded during play sessions with an assistant who engaged in relatively low(15%) or relatively high rates(40%) of mixing on three separate occasions. The results indicate that these children were sensitive to the language choices of their interlocutors and that they were able to adjust their rates of mixing accordingly; further, they appeared to do this by matching their language choice with that of their interlocutors on a turn-by-turn basis.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".