Young children's use of range‐of‐reference information in word learning
Bibliographic record
Abstract
An important source of information about a new word's meaning (and its associated lexical class) is its range of reference: the number of objects to which it is extended. Ninety toddlers (mean age = 37 months) participated in a study to determine whether young children can use this information in word learning. When a novel word was presented with unambiguous lexical class cues as either a proper name (i.e. 'His name is DAXY') or an adjective (i.e. 'He is very DAXY'), toddlers interpreted it appropriately, regardless of whether it was applied to one or both members of a pair of identical-looking stuffed animals. They restricted a proper name to the designated animal(s); but they generalized an adjective from the labeled animal(s) to a new animal bearing the same property. However, when the word was presented with no specific lexical class cues (i.e. 'DAXY'), toddlers made significantly different interpretations, depending on the number of referents. When the word was applied to one animal, they restricted it to that animal (consistent with a proper name interpretation); when the word was applied to two animals, they generalized it to a new animal with the property (consistent with an adjective or a restricted count noun interpretation). Range-of-reference information thus provided toddlers with a default cue to the meaning (and associated lexical class) of a novel word.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".