Semantic and Conceptual Knowledge Underlying Bilingual Babies' First Signs and Words
Bibliographic record
Abstract
We addressed the question of how babies exposed to two languages simultaneously acquire the meanings of words across their two languages. In particular, we attempted to shed new light on whether babies know that they are acquiring different lexicons right from the start, or whether early bilingual exposure causes them to be semantically confused. We propose a collection of research methods that, taken together, can answer these questions, which have hitherto received scant attention. Six hearing babies were videotaped for one hour on average seven times over one year (ages ranging from 0;7 to 2;2); three babies were acquiring French and English, and three French and LSQ. These populations offer unique insights into the semantic knowledge underlying bilingual as well as monolingual language acquisition. We found that the babies (1) acquired their two languages on the same timetable as monolinguals and (2) produced translation equivalents in their very first lexicons. Further, their early words (signs) in each language (3) were constrained along kind boundaries, (4) showed fundamentally similar semantic organization across their dual lexicons, and (5) reflected the meanings of their favorite things first. We also discuss why attributions that young bilinguals are delayed and confused have prevailed and we show that they are neither at this point in development. Finally, the present findings show how research of this type can provide a method for making bilingual norms wholly attainable.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".