The Effect of Input on Children’s Cross-Categorical Use of Polysemous Noun-Verb Pairs
Bibliographic record
Abstract
Using an observational task followed by an experimental task with an Intermodal Preferential Looking Paradigm, we examined the effect of input on children’s acquisition of class extension rules by investigating the relationship between the amount of polysemous noun-verb pairs in French-speaking 2-year-olds’ input and both their spontaneous production of these words and their comprehension of novel instances of these words. Study 1 demonstrated that the number of words children used cross-categorically was related to the number of words their mothers used cross-categorically. Children also used object-denoting words cross-categorically more often than nonobject- and action-denoting words. Study 2 demonstrated that only children whose mothers frequently used noun-verb pairs cross-categorically were able to understand the cross-categorical use of the novel object-denoting words in the experimental task. This suggests that semantic and distributional cues associated with object-denoting noun-verb pairs in the input play an important role in children’s acquisition of class extension rules.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".