Selective skepticism: American and Chinese children's reasoning about evaluative academic feedback.
Bibliographic record
Abstract
Children's reasoning about the credibility of positive and negative evaluations of academic performance was examined. Across 2 studies, 7- and 10-year-olds from the United States and China (N = 334) judged the credibility of academic evaluations that were directed toward an unfamiliar peer. In Study 1, participants from China responded that criticism should be accepted to a greater extent than did participants from the United States, and children from both countries demonstrated a selective skepticism effect by treating negative feedback more skeptically than positive feedback. Study 2 replicated the selective skepticism effect among children from both countries and ruled out the possibility that it can be explained as a rational analysis of perceived base rates. The results suggest that children are selective in their trust of evaluative feedback and that their credibility judgments may be influenced by the desirability of the information that is being conveyed or its anticipated consequences.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".