Comparison, basic-level categories, and the teaching of adjectives
Bibliographic record
Abstract
We tested 24 caregivers of preschool children to determine whether their strategies for teaching novel adjectives are consistent with children's demonstrated abilities to learn these words (e.g., Waxman & Klibanoff, 2000). On each of four trials, caregivers had to select one of two cards, both of which showed a familiar object bearing an unfamiliar property. On the within-basic card, the object was accompanied by a second object from the same basic-level category; on the across-basic card, this second object came from a different basic-level category. Caregivers' task was to choose the card that would be more helpful to teach a novel adjective for the unfamiliar property. If the second object differed from the first in terms of a novel target property, caregivers (N = 12) stated a strong preference for the within-basic card. If the two objects agreed in terms of the novel property, caregivers (N = 12) indicated a clear preference for the across-basic card. The findings offer new insight into the speed and efficiency of lexical development, by revealing that word teachers, like word learners (cf. Waxman & Klibanoff, 2000), are sensitive to the conditions under which certain contrasts (in property or in basic-level category) are effective in promoting the successful acquisition of 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".