Investigating naturalistic code-switching directed towards infants
Bibliographic record
Abstract
Mixing two languages in speech (i.e., code-switching) is prevalent in multilingual settings, including in speech directed towards infants. Prior research suggests a link between parental code-switching and vocabulary size (Byers-Heinlein, 2013). Moreover, laboratory work suggests that some types of code-switching appear more difficult for infants to process than others (Byers-Heinlein et al., 2017; Potter et al., 2018). This raises the possibility that the effects of parental code-switching depend on the parents’ specific behavior in terms of the frequency, location, and purpose of code-switching (Byers-Heinlein, 2017). Prior studies of parental code-switching relied on self-report or short lab observations. In this study, we analyze parental code-switching behavior in a corpus of daylong home recordings of 21 infants (at 10- and 18-months) from French-English bilingual families in Montréal. We will identify instances of parental code-switching, their syntactic location, the direction of the switch, and the apparent reason for the switch (e.g., teaching vocabulary, translating an entire utterance). Preliminary results indicate that the frequency of code-switching varies between families and that code-switching between sentences is more common than code-switching within a sentence. This project will provide the first in-depth investigation about the characteristics of naturally produced parental code-switching.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".