Aware and tuned to care: Children with better distress recognition and higher sympathy anticipate more guilt after harming others
Bibliographic record
Abstract
Helping children recognize the distress of their victims and feel sympathy may facilitate the optimal socialization of ethical guilt. With a sample of 150 eight-year-olds, we tested the main and interactive relations of distress recognition and sympathy to ethical guilt after hypothetically stealing and pushing. Better fear recognition and higher sympathy were uniquely associated with higher ethical guilt. The link between fear recognition and ethical guilt was stronger in children with higher sympathy. Beyond their unique contributions, distress recognition and sympathy may work in concert to facilitate ethical guilt after harming others. Statement of contribution What is already known on this subject Children are thought to express more guilt if they recognize their victims' distress and feel sympathy for them. However, there is little evidence for the direct roles of distress recognition and sympathy in children's guilt, and none for their joint contribution. What the present study adds The link between fear recognition and guilt was stronger in children with higher sympathy. Sympathy may help children harness and translate the awareness afforded by distress recognition into feelings of accountability and regret. This study was the first to clarify the main and additive roles of sympathy and distress recognition in children's anticipation of guilt after harming others. Promoting distress recognition and sympathy may represent a viable two-step approach to inducing guilt in children after they violate others' welfare.
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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) | 0.000 | 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 | 0.001 |
| 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".