Bilingual first language acquisition: exploring the limits of the language faculty
Bibliographic record
Abstract
Most general theories of language acquisition are based on studies of children who acquire one language. A general theory of language acquisition must ultimately accommodate the facts about children who acquire two languages simultaneously during infancy. This chapter reviews current research in three domains of bilingual acquisition: pragmatic features of bilingual code-mixing, grammatical constraints on child bilingual code-mixing, and bilingual syntactic development. It examines the implications of findings from these domains for our understanding of the limits of the mental faculty to acquire language. Findings indicate that infants possess the requisite neuro-cognitive capacity to differentially represent and use two languages simultaneously from the one-word stage onward, and probably earlier. Detailed analyses of the syntactic organization of bilingual child language indicates, moreover, that it conforms to the target systems and, thus, resembles that of children acquiring the same languages monolingually, for the most part. At the same time, bilingual children acquire the distinctive capacity to coordinate their two languages in grammatically constrained ways and in conformity with the target grammars during online production. In short, current evidence attests to the bilingual capacity of the human mind and refutes earlier conceptualizations which viewed bilingualism and bilingual acquisition as burdensome and potentially disruptive to development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| 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".