The Impact of Codemixing on Language Differentiation in Young Bilinguals
Bibliographic record
Abstract
The phenomenon by which a lexical item or phrase from one language is inserted into another, known as codemixing, is common in adult bilingual communities around the world (Genesee & Nicoladis, 1997). In many types of immersion programs as well, codemixing is a common strategy for introducing target vocabulary. However, little research has been conducted on the precise impact that vocabulary exposure via codemixing may have on how the target item is encoded by child listeners – namely, how it is assigned to one language or another. Spanish- and English-speaking children (n = 10) between 3 and 6 years old were recruited to participate in this experiment, in which phonetically English- or Spanish-apparent nonwords were presented in the context of a “codemixed” or “non-codemixed” sentence and participants were asked to decide to which language the nonword belonged. Results demonstrated a considerable bias toward categorizing most of the nonwords as Spanish (the non-dominant language for all ten children), although the language in which the nonword was introduced also considerably impacted children’s judgments. While the nonword’s phonology appears somewhat influential in determining its language of origin, this was not as impactful as the overall linguistic context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".