The effect of emotional dissonance and emotional intelligence on work–family interference.
Bibliographic record
Abstract
In this study, we examined the relationship between emotional dissonance and work-to-family inference (WFI) and whether emotional intelligence moderated the association between emotional dissonance and WFI. Data were collected at two time points. At Time 1 (T1), we measured emotional dissonance, demographic variables (i.e., gender, age, marital status, number of children), negative affectivity, emotional intelligence, and WFI (T1). At Time 2 (T2), WFI was measured again. A total of 155 valid questionnaires were collected at two time points. Hierarchical regression analyses showed that emotional dissonance at T1 was a salient predictor of WFI at Time 2, even when WFI at Time 1 and other variables were controlled. One subdimension of emotional intelligence-namely regulation of emotion-was also significantly related to WFI at T2. However, emotional intelligence did not moderate the association between emotional dissonance and WFI.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".