Bibliographic record
Abstract
Previous research shows that infants being raised in single-language families have some basic language discrimination abilities at birth, that these skills improve over the first 6 months of life, and that infants are attending to the rhythmic properties of language to perform these skills. Research has also revealed that newborns and older babies from monolingual families prefer listening to their native language over an unfamiliar language. Data on language discrimination and preference in bilingual infants is very limited but is necessary to determine if the patterns and rate of bilingual language development parallel those of monolingual development, or if exposure to more than one language modifies developmental patterns. The present study addresses this issue by comparing language preference in monolingual English, monolingual French, and bilingual English–French infants between 3 and 10 months of age. Infant preference to listen to passages in three rhythmically different languages (English, French, Japanese) was assessed using a visual fixation procedure. Passages were produced by three female native speakers of each language. Findings will show how native language preference is affected by age and language experience in infants who experience monolingual and bilingual language exposure.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".