Quantifying child directed speech cross-culturally across development
Bibliographic record
Abstract
Child-directed speech (CDS) influences language development (e.g., Golinkoff et al., 2015), but varies across cultural and demographic groups (Hoff, 2006). Recent work examining speech heard by North American English (NAE) infants found an increased proportion of CDS with age (Bergelson et al., 2018). Quantity of CDS remained relatively constant across age, while quantity of adult-directed speech (ADS) decreased. We replicate these findings using a different methodology, and expand them to include other language communities. Our data come from daylong audio recordings of 58 children ages 2–36 months from the ACLEW dataset (Bergelson et al., 2017; 30 children acquiring NAE, 10 UK English, 8 Argentinian Spanish, and 10 Tseltal/Mayan). Ten randomly selected 2-min segments (Tseltal: nine 5-min segments) from each child were annotated for speaker gender, age (child or adult), and addressee for each utterance. We calculated the minutes per hour of CDS, ADS, and all speech. Preliminary analyses find high variability in overall language input across individuals, age, and culture, and partially replicate the Bergelson et al. (2018) pattern of results. Ongoing annotation will permit finer-grained analyses of sub-group differences. Further analyses will examine the influence of factors such as speaker gender, number of speakers, and maternal education.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".