Telling young children they have a reputation for being smart promotes cheating
Bibliographic record
Abstract
The present research examined the consequences of telling young children they have a reputation for being smart. Of interest was how this would affect their willingness to resist the temptation to cheat for personal gain as assessed by a temptation resistance task, in which children promised not to cheat in the game. Two studies with 3- and 5-year-old children (total N = 323) assessed this possibility. In Study 1, participants were assigned to one of three conditions: a smart reputation condition in which they were told they have a reputation for being smart, an irrelevant reputation control condition, or a no reputation control condition. Children in the smart reputation condition were significantly more likely to cheat than their counterparts in either control condition. Study 2 confirmed that reputational concerns are indeed a fundamental part of our smart reputation effect. These results suggest that children as young as 3 years of age are able to use reputational cues to guide their behavior, and that telling young children they have a positive reputation for being smart can have negative consequences.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".