The basis of preference for lexical words in 6‐month‐old infants
Bibliographic record
Abstract
Abstract Six‐month‐old English‐learning infants have been shown to prefer English lexical over English grammatical words. The preference is striking because there are few grammatical words in total number but each occurs far more frequently in input speech than any individual lexical word. This could be because lexical words are universally more salient and interesting acoustic and phonological forms than are grammatical words. Alternatively, familiarity may play a role since infants may know some specific lexical words. Here we explore the first possibility by testing Chinese‐learning infants’ response to English lexical and grammatical words. These infants, who had virtually no prior exposure to English and thus were unfamiliar with any English words, nevertheless preferred to listen to English lexical words, as in the case of English‐learning infants. This finding increases the plausibility that it is the acoustic and phonological salience of lexical words that determines the preference for lexical words in infants.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".