Preschoolers use helpful and harmful interactions to predict social category membership.
Bibliographic record
Abstract
= 229) were introduced to 2 novel social categories (comprised of diverse individuals), each possessing a distinct property (e.g., dancing vs. singing). During test trials, children witnessed members from each category direct helpful, harmful, or neutral actions toward ambiguous characters. In Experiment 1, when a member of a category exhibited helpful actions toward an ambiguous character, children inferred that the ambiguous character belonged to the same category as the helper. In contrast, when a group member directed harmful actions toward the ambiguous character, children inferred that ambiguous character belonged to the alternate category. In neither case did children extend category properties to the new member. In Experiment 2, category properties were introduced as mutually exclusive social conventions. Here, children used helpful and harmful actions to identify the social category of an ambiguous character and generalize the property. In Experiment 3, when social categories were introduced using only shared physical location and shared activity as a marker of category membership, children did not use helpful or harmful behavior to predict category membership. Together findings indicate that children can identify social category membership based on observed social interactions when meaningful category information is available. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".