Social‐Emotional Responding: A Perspective from Developmental Psychology
Bibliographic record
Abstract
Abstract Social‐emotional responding(SER) refers to (i) an individual's awareness and understanding of emotional experiences in the self and others, (ii) expression of emotions, and (iii) emotion regulation capacities. The normative development of these responses is considered a central component of human development. This is because SER underlies our capacities to express other‐oriented behaviors and cope with challenges of everyday life in adaptive and socially responsible ways. The goal of this essay is to identify emerging trends in this area of developmental research. We first discuss central conceptual issues in social‐emotional development and present a conceptual framework from developmental psychology to study SER. Next, we identify current shortcomings in research on SER. We focus on three central components of SER: self‐conscious emotions, other‐oriented emotions, and emotion regulation. On the basis of our analyses of the current gaps, we highlight three promising attempts to solve some of the current shortcomings in this literature: attempts to understand developmental relations among self‐conscious emotions, other‐oriented emotions, and emotion regulation capacities; attempts to identify psychological, neural, and behavioral mechanisms underlying social‐emotional responding, and; the application of this knowledge to interventions that concern children and families.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".