Testing theories of the vocabulary spurt with monolingual and bilingual infants.
Bibliographic record
Abstract
Sometime before their second birthday, many children have a period of rapid expressive vocabulary growth called the vocabulary spurt. Theories of the underlying mechanisms differ: Accumulator models emphasize the accumulation of experience with words over time to yield a spurtlike pattern, while cognitive models attribute the spurt to cognitive changes. To test these theories, English-French monolingual and bilingual children with different exposure to each language were studied. Dense, longitudinal data were analyzed from 45 infants aged 16-30 months, whose expressive vocabulary was measured on a total of 617 occasions in English and/or French. Single-language (English and/or French), concept (number of concepts lexicalized across both languages), and word (sum of both languages) vocabulary scores were computed. Infants' exposure to each language and their exposure balance were measured using a language exposure questionnaire. Logistic curves were fitted to each infant's data to estimate the timing (midpoint) and steepness (slope) of the vocabulary spurt in single-language, concept, and word vocabularies. Seventy-six percent of infants showed a spurt in at least one vocabulary type, and bilinguals were less likely to show one in their nondominant than their dominant language. For single-language vocabulary, infants with more exposure to a language had earlier spurts. For combined vocabularies (concept and word), monolinguals and unbalanced bilinguals had earlier and steeper spurts than balanced bilinguals. Results better support the predictions of accumulator models than cognitive theories and show that infants follow different vocabulary acquisition trajectories based on their language background. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".