Predicting two Mandarin-English bilingual children’s first 50 words: Effects of frequency and relative exposure in the input
Bibliographic record
Abstract
This study tested two hypotheses that relate children’s early words to the input: (1) the language of bilingual children’s first words is correlated with their exposure time to each language and (2) children are more likely to acquire words from the first and/or last position in utterances. We tested these hypotheses with spontaneous language use in the families of two Mandarin—English bilingual children, followed longitudinally every three months from 6 months to 18 months. The results showed that the exposure time was a good but not perfect predictor of the language of the children’s first 50 words. Also, both children learned more nouns in both languages than appeared in first or last position of their caregivers’ utterances. We discuss these results in terms of support for a noun bias in children’s minds and highlight the need for future research to take into account socio-cultural variables.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".