Quantifying infants' statistical word segmentation: a meta-analysis.
Bibliographic record
Abstract
Theories of language acquisition and perceptual learning\nincreasingly rely on statistical learning mechanisms. The\ncurrent meta-analysis aims to clarify the robustness of this\ncapacity in infancy within the word segmentation literature.\nOur analysis reveals a significant, small effect size for\nconceptual replications of Saffran, Aslin, & Newport (1996),\nand a nonsignificant effect across all studies that incorporate\ntransitional probabilities to segment words. In both\nconceptual replications and the broader literature, however,\nstatistical learning is moderated by whether stimuli are\nnaturally produced or synthesized. These findings invite\ndeeper questions about the complex factors that influence\nstatistical learning, and the role of statistical learning in\nlanguage acquisition.
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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 | 0.025 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".