Emotion Regulation in Workgroups: The Roles of Demographic Diversity and Relational Work Context
Bibliographic record
Abstract
Drawing on the social identity perspective, we investigate the cross‐level relationship between demographic diversity in workgroups and emotion regulation. We propose that age, racial, and gender diversity in workgroups relate positively to emotion regulation because of demography‐related in‐group/out‐group dynamics. We also examine the moderating role of the relational work context, specifically task interdependence and social interaction, on the relationship between demographic diversity and emotion regulation. Results from a sample of 2,072 employees in 274 workgroups indicate that working in a group with greater age diversity is positively related to an employee's emotion regulation. Results suggest the operation of the age diversity effect can be attributed primarily to younger employees when they are in workgroups with older coworkers. Results reveal asymmetric effects for racial diversity such that racial out‐group members engage in higher levels of emotion regulation than racial in‐group members when racial diversity is low, whereas racial in‐group members engage in higher levels of emotion regulation than racial out‐group members when racial diversity is high. Race effects also suggest a moderating effect of social interaction; specifically, social interaction weakens the relationship between racial diversity and emotion regulation. Gender effects are not significant.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".