French-English bilingual children’s motion event communication shows crosslinguistic influence in speech but not gesture
Bibliographic record
Abstract
Abstract Bilinguals sometimes show crosslinguistic influence from one language to another while speaking (or gesturing). Adult bilinguals have also shown crosslinguistic influence in gestures as well as speech, suggesting an underlying conceptualization that is similar for both languages. The primary purpose of the present study is to test if the same is true of simultaneous French-English bilingual children in speaking and gesturing about motion. If so, they might show different patterns from both French and English monolinguals. Furthermore, we examined whether there were developmental changes between early and middle childhood. French-English bilingual and French and English monolingual children watched two cartoons and described them. In speech, the bilinguals differed from the English monolinguals, using more lexicalizations of the Path of motion in token numbers but not in type. They did not differ from the French monolinguals. In gestures, all children used a majority of Path gestures. There were few age-related changes. We argue that in speech, the bilinguals conceptualize their two languages differently, but show some crosslinguistic influence due to processing. Gestures may not show this same pattern, because they serve to highlight the important parts of the discourse.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".