Children's use of frequency information for trait categorization and behavioral prediction.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Two experiments examined young children's use of behavioral frequency information to make behavioral predictions and global personality attributions. In Experiment 1, participants heard about an actor who behaved positively or negatively toward 1 or several recipients. Generally, children did not differentiate their judgments of the actor on the basis of the amount of information provided. In Experiment 2, the actor behaved positively or negatively toward a single recipient once or repeatedly. Participants were more likely to make appropriate predictions and attributions after exposure to multiple target behaviors and with increasing age. Overall, children's performance was influenced by age-related positivity and negativity biases. These findings indicate that frequency information is important for personality judgments but that its use is affected by contextual complexity and information-processing biases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it