Children's preference for HAS and LOCATED relations: A word learning bias for noun–noun compounds
Bibliographic record
Abstract
The present study investigates children's bias when interpreting novel noun-noun compounds (e.g. kig donka) that refer to combinations of novel objects (kig and donka). More specifically, it investigates children's understanding of modifier-head relations of the compounds and their preference for HAS or LOCATED relations (e.g. a donka that HAS a kig or a donka that is LOCATED near a kig) rather than a FOR relation (e.g. a donka that is used FOR kigs). In a forced-choice paradigm, two- and three-year-olds preferred interpretations with HAS/LOCATED relations, while five-year-olds and adults showed no preference for either interpretation. We discuss possible explanations for this preference and its relation to another word learning bias that is based on perceptual features of the referent objects, i.e. the shape bias. We argue that children initially focus on a perceptual stability rather than a pure conceptual stability when interpreting the meaning of nouns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".