Young children's use of syntactic cues to learn proper names and count nouns.
Bibliographic record
Abstract
In 6 experiments, 144 toddlers were tested in groups ranging in mean age from 20 to 37 months. In all experiments, children learned a novel label for a doll or a stuffed animal. The label was modeled syntactically as either a count noun (e.g., "This is a ZAV") or a proper name (e.g., "This is ZAV"). The object was then moved to a new location in front of the child, and a second identical-looking object was placed nearby. The children's task was to choose 1 of the 2 objects as a referent for the novel word. By 24 months, both girls (Experiment 2) and boys (Experiment 5) were significantly more likely to select the labeled object if they heard a proper name than if they heard a count noun. At 20 months, neither girls (Experiments 1 and 6) nor boys (Experiment 1) demonstrated this effect. By their 2nd birthdays, children can use syntactic information to distinguish appropriately between labels for individual objects and those for object categories.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".