Bibliographic record
Abstract
There is growing concern about the replicability of basic findings in psychology, including in infancy research (Frank et al., 2017). ManyBabies is a large-scale international collaboration to replicate basic empirical findings in infancy. Our first project is ManyBabies1, which examines infant preference for infant-directed speech (IDS). Over 70 laboratories collaborated to collect data from over 2500 infants aged 3–15 months. Stimuli consisted of speech produced in a semi-naturalistic elicitation task where fifteen different mothers who spoke North American English talked about a series of novel and familiar objects to their infant and separately to an experimenter. A set of 8 passages each in IDS and adult-directed speech (ADS) were created after a comprehensive norming process. Infants were tested in 3 primary methods: eyetracking, central fixation and headturn preference. Overall, the effect of preference for IDS was replicated, although the calculated effect was smaller than that reported by meta-analysis (Dunst et al., 2012, metalab.stanford.edu). Preference for IDS increased across development. The success of this project shows that infant research conducted collaboratively and at-scale can answer new questions about the replicability and generalizability of infant findings across different laboratory, methodological, and infant participant characteristics.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".