Gender and Emotions: Intra- and Interpersonal Effects of Emotional Ambivalence and Compassion
Bibliographic record
Abstract
The focus of this proposed symposium is to examine the effects of experiencing and expressing emotional ambivalence and compassion at work on important outcomes, and critically, how these effects vary depending on gender. We join four investigations that document new phenomena at the nexus of gender, emotional experience, emotional expression, emotional display rules, leader effectiveness, leader behavior, leadership emergence, and performance outcomes. Skeptical Responses to Male and Female CEOs’ Expressions of Emotional Ambivalence Presenter: Corinne A. Post; Villanova U. Presenter: Naomi Beth Rothman; Lehigh U. Presenter: McKay Price; Lehigh U. Presenter: James Cicon; U. of Central Missouri Feeling One Thing and Feeling Another: Gender Differences in the Experience of Emotional Ambivalence Presenter: Jamie Strassman; U. of Texas at Austin When and Why Emotional Ambivalence is Beneficial (and Harmful) in Women Leaders Presenter: Naomi Beth Rothman; Lehigh U. Presenter: Jeffrey Sanchez-Burks; U. of Michigan Presenter: Jean-Nicolas Reyt; McGill U. The Social Consequences of Compassion: Effects of Gender Differences and Compassion Types on Leaders Presenter: Reut Livne-Tarandach; Manhattan College Presenter: Sophie Pychlau; U. of Oregon Presenter: Angela R. Grotto; Manhattan College Presenter: Poonam Arora; Manhattan College
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".