Everyday language input and production in 1001 children from 6 continents
Bibliographic record
Abstract
Language is a universal human ability, acquired readily by young children who otherwise struggle with many basics of survival1,2. And yet, language is variable across individuals. Behavioral and experimental observations suggest that children’s linguistic skills vary with factors like socioeconomic status3, children’s gender4, and multilingualism5. But which factors really influence children’s day-to-day language use? Here we leverage speech technology in a big-data approach to report on a unique cross-cultural and diverse data set: >2,500 day-long, child-centered audio-recordings of 1,001 2- to 48-month-olds from 12 countries spanning 6 continents across urban, farmer-forager, and subsistence-farming contexts. As expected, age and language-relevant clinical risks and diagnoses6 strongly correlated with how much speech (and speech-like vocalization) children produced. Critically, so too did adult talk in children’s environments: Children who heard less talk from adults produced less speech. In contrast to previous conclusions based on more limited sampling methods and a different set of language proxies, socioeconomic status, child gender, and multilingualism were not associated with children’s productions over the first four years of life. These findings from large-scale naturalistic data advance our understanding of what factors are robust predictors of variability in language behaviors of young learners in a wide range of everyday contexts.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".