Children's Use of Syntactic and Pragmatic Knowledge in the Interpretation of Novel Adjectives
Bibliographic record
Abstract
In Study 1, English-speaking 3- and 4-year-olds heard a novel adjective used to label one of two objects and were asked for the referent of a different novel adjective. Children were more likely to select the unlabeled object if the two adjectives appeared prenominally (e.g., "a very DAXY dog") than as predicates (e.g., "a dog that is very DAXY"). Study 2 revealed that this response occurred only when both adjectives were prenominal. Study 3 replicated Study 1 with Hebrew-speaking 3- and 4-year-olds, even though in Hebrew both types of adjectives appear postnominally. Preschoolers understand that prenominal adjectives imply a restriction of the reference of nouns, and this knowledge motivates a contrastive pragmatic inference regarding the referents of different prenominal adjectives.
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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 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".