Correlates of children's sympathy: Recognition and regulation of sadness and anger
Bibliographic record
Abstract
Abstract Emotion recognition and emotion regulation have been implicated as promising correlates of sympathy. However, their relative and joint contributions to sympathy in different periods of childhood remain unclear. Moreover, researchers have not explored the relative importance of recognizing and regulating distinct distress‐related emotions, such as sadness and anger, for sympathy. The objective of the current paper was to gain a deeper understanding of which underlying emotion‐related factors are most implicated in sympathy and when in childhood they are most predictive. With an ethnically diverse sample of 4‐ and 8‐yearolds (N = 300, n = 150 in each age group; 50% female), this study tested sadness and anger recognition and regulation, and interactions thereof, as predictors of sympathy. Better sadness and anger regulation independently predicted higher levels of sympathy in 4‐ and 8‐year‐olds, albeit sadness regulation was a more robust predictor of sympathy in 4‐year‐olds. Better sadness recognition was associated with higher sympathy in 8‐year‐olds who also had better sadness regulation. Results underscore the importance of emotion regulation for sympathy, particularly in early childhood. The findings also tentatively suggest that the correlates of sympathy may become more nuanced in middle childhood, with emotion‐specific recognition and regulation capacities employed in concert.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".