Emotional Development and Regulation in Children: A Review of Recent Advances
Bibliographic record
Abstract
The article aims to explore the complexities of emotional development and regulation in children, emphasizing the impact of various factors including familial, educational, cultural, and technological influences. It seeks to understand how these elements contribute to children's emotional competence and socioemotional adjustment. This review synthesizes recent research findings from interdisciplinary studies, employing a comprehensive analysis of literature that spans psychology, education, and digital technology. It includes observational studies, experimental research, and qualitative analyses to provide a broad overview of the field. Key findings highlight the significance of emotional competence in socioemotional development, the role of caregivers and educators in shaping emotional regulation, the influence of cultural and environmental factors, and the potential of digital technologies in supporting emotional learning. It also discusses challenges in emotional regulation faced by children with developmental conditions. The article concludes that emotional development and regulation are influenced by a dynamic interplay of individual, social, and technological factors. It calls for a multidisciplinary approach to research and practice, emphasizing the need for innovative, inclusive interventions that support the emotional well-being of children across different contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".