How Cultural Input Shapes the Development of Idealized Biological Prototypes
Bibliographic record
Abstract
Young children in the U.S. tend to hold narrow, idealized prototypes for animal and social categories, focusing on ideas about how categories should be and ignoring category variability. The current studies tested how children’s (N = 281) reliance on idealized prototypes might be shaped by adults’ communication of common essentialist and teleological biases. In Study 1, 7- to 8-year-old U.S. children viewed more average members of novel animal categories as prototypical when they heard a teacher correct a generic statement about a characteristic feature and highlight how varied features serve varied functions. In Study 2, explanations about varied functions alone explained this effect for novel animals, with mixed effects for familiar animals; there was no additive effect of correcting generic language. Children in Study 2 also expected functionally ideal features to be more frequent among category members, suggesting that idealized prototypes reflect mistaken assumptions that category members homogeneously share ideal features. Children in Study 2 did not explicitly disapprove of nonconformity, suggesting that idealized prototypes do not reflect an inability to dissociate how things are from how they should be. Together, these results support the proposal that U.S. children’s idealized prototypes are shaped by common conceptual biases perpetuated by cultural input.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".