Hot dogs and zavy cats: Preschoolers’ and adults’ expectations about familiar and novel adjectives
Bibliographic record
Abstract
In recent years, a growing body of research has begun to examine the processes that underlie young children's acquisition of adjectival meanings. In the present studies, we examined whether preschoolers' willingness to extend adjectives was influenced by the type of property labeled by familiar adjectives (Experiment 1) and by semantic information conveyed in the sentence used to introduce novel adjectives (Experiment 2). In Experiment 1, we examined preschoolers' and adults' expectations about the generalizability of familiar adjectives of three different types: emotional state terms, physiological state terms, and stable trait terms. On each trial, we labeled a target animal with one of the three different types of adjectives and asked whether these terms could apply to a subordinate-level match, a basic-level match, a superordinate-level match, or an inanimate object. Results indicated that 4-year-olds and adults extended the trait terms, but not the emotional or physiological terms, to members of the same basic-level category. In Experiment 2, we presented 4-year-olds and adults with novel adjectives in one of two verb frames: stable ("This X is very daxy") or transient ("This X feels very daxy"). Participants were more likely to extend the novel adjective to subordinate matches if they were in the Stable frame group than if they were in the Transient frame group. These findings are discussed in terms of implications for young children's expectations about familiar and novel 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".