Men’s Allyship in Organizations: Antecedents, Outcomes, and Contingencies
Bibliographic record
Abstract
Men's allyship could be one of the driving forces for enhancing gender equality in organizations. Yet, research on the antecedents, outcomes, and contingencies of men's allyship is still scarce. With our symposium, we aim to illustrate ongoing research projects on men’s allyship in organizational contexts and beyond via four research presentations. Warren and colleagues explore whether a man’s character informs which allyship strategies he perceives as authentic and well- fitting. Barthel examines which effect leaders’ allyship advocacy has on male employees’ proactive allyship intention and behavior and which role leader gender plays in this relationship. Hagl and colleagues investigate differences in how women and men perceive the motives and values underlying men’s allyship behavior. De Souza and Schmader explore women’s allyship experiences in STEM and whether allyship towards women can only be enacted by men or also by high status individuals more generally. The discussant Toni Schmader will focus on the distinct forms that allyship can take, the consequences of authentic and performative allyship, and the barriers to engaging in allyship behavior. Character Strengths and Allyship: Exploring Character Profiles that Position Men to Address Bias Author: Meghana Warren; Western Washington University Author: Michael T Warren; Western Washington University Author: Ryan M. Niemiec; - Author: Camille A. Fogel; Western Washington University The Effect of Leaders’ Allyship Storytelling on Male Employees: Does Leader Gender Matter? Author: Anna Dorothea Tabea Barthel; Christian Albrecht University of Kiel Allyship in the Eye of the Beholder: The Influence of Ally Values and Motivations Across Genders Author: Regina Hagl; Technical University of Munich Author: Ronit Kark; Bar-Ilan University Author: Ben Shalom Cohen; Bar-Ilan University Author: Claudia Buengeler; Christian Albrecht University of Kiel Gender, Status, and Allyship: A Qualitative Study of Women’s Lived Experiences of Allyship in STEM Author: Lucy De Souza; The University of British Columbia Author: Toni Schmader; The University of British Columbia
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".