Input and word learning: caregivers' sensitivity to lexical category distinctions
Bibliographic record
Abstract
Twenty-four caregivers and their two- to four-year-old children took part in a storybook reading task in which caregivers taught children novel labels ('DAXY') for familiar objects. One group (N = 12) received labels modelled syntactically as proper names ('This is named DAXY'), and another group (N = 12) received the same labels for the same objects modelled syntactically as adjectives ('This is very DAXY'). Caregivers took strikingly different approaches to teaching words from the two lexical categories. In teaching proper names, but not adjectives, caregivers flagged cases in which one word was paired with two objects; two words were paired with one object; and one word was paired with an inanimate object. In teaching adjectives, but not proper names, caregivers discussed meaning and offered translations. Caregivers' distinctive strategies for teaching proper names and adjectives are congruent with recent findings about children's word meaning assumptions, and with analyses of the semantics of these lexical categories. The findings indicate that parental speech could provide a rich source of information to children in learning how different lexical categories are expressed in their native language.
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".