Talking About Our Own Emotions and the Emotions of Others at Work
Bibliographic record
Abstract
Emotions have important implications for social interaction in the workplace. However, research has primarily focused on the effects of non-verbal displays of one’s own emotions and responses to the emotions of others. Important questions remain regarding the consequences of how individuals talk about their own emotions and the emotions of others at work. The five papers presented cover a broad range of interrelated topics (e.g., collective emotion regulation, verbal emotional expression) and represent different theoretical and empirical perspectives. Our discussant, Stéphane Côté, a leading scholar in the study of emotions, will close our session by offering a synthesis of papers and discussing with the audience future directions for the study of talking about emotions in the workplace. Through this symposium, we aim to generate new insights about how scholars can continue to study and improve the research on talking about the emotions of oneself and others at work. Emotional Expression and Exploitation Author: Jeremy Yip; Georgetown U., McDonough School of Business Author: Kelly Lee; - Leader Emotional Explication: Leaders Explaining the Reasons for Their Emotions Affects Followers Author: Christina Bradley; U. of Michigan, Ross School of Business Author: Gerben Alexander Van Kleef; U. of Amsterdam More than Fashion: Nostalgia’s Rose-Colored Glasses for Crisis and Interpersonal Emotion Management Author: Emily Hsu; Washington U. in St. Louis, Olin Business School Author: William Bottom; Washington U. in St. Louis Positive Empathy Emerges When People Neurobiologically Sync Up Author: Olivia Jurkiewicz; U. of California, San Diego Author: Yumeng Gu; U. of California, San Diego Author: Isaac Raymundo; Columbia Business School Author: Christopher Oveis; U. of California, San Diego Strategy but not Goal Determines Group Emotion Regulation Effectiveness Author: Yajun Cao; Harvard Business School Author: Amit Goldenberg; Harvard Business School
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".