Internalizing emotions: Self-determination as an antecedent of emotional intelligence
Bibliographic record
Abstract
An extensive body of literature indicates that people differ in the extent to which they attend to, process, and regulate emotions. The present research sought to build on this knowledge by examining whether general self-determination (GSD) could account for individual variation in emotional intelligence (EI) and psychological well-being (PWB). A simple and multiple mediation model using bootstrap analyses tested these relationships in a sample of students (Study 1, N = 283) and workers (Study 2, N = 265). Results supported the hypothesized mediating role of EI in the relationship between GSD and PWB across both studies. When the inter-related facets of EI were considerately separately, indirect effects emerged for mood regulation/optimism and social skills across both studies as well as for utilization of emotions, albeit negatively, in Study 2. Our findings support and extend past work on the antecedents of EI and have important implications for human functioning across a variety of settings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".