ManyBabies1 part 2: Influences of language experience on infant-directed speech preference
Bibliographic record
Abstract
ManyBabies1, our first effort at a large scale collaborative infant experimental study, provided a conceptual replication of the well-known phenomenon of infant preference for the characteristics of Infant-directed speech (IDS). One important question that has largely been unanswered by extant literature is how much the IDS preference is dependent on experience with a specific language. How do infants respond to IDS that is in a non-native variety, and how does their listening affect this preference? ManyBabies 1 used a consistent stimulus set of North-American English (NAE), which allowed us to answer this questions using two approaches. First, because participating ManyBabies 1 labs were located around the world, we were able to compare monolingual infants from a range of native-language backgrounds. We found that the preference for North American English IDS was larger for infants whose native language was NAE than for infants who had a different native language. Second, we conducted a sister project, ManyBabies 1 Bilingual, which tested infants from a variety of bilingual backgrounds. Bilinguals have similar total language experience and maturation as monolinguals, but their experience is divided across two or more languages. Planned analyses will examine monolingual-bilingual differences, and “dose-response” effects of exposure to NAE.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".