Targets, Actors, and Observers: Adding Perspective and Nuance to Allyship Engagement
Bibliographic record
Abstract
As workplaces continue to grapple with persistent inequities, paired with growing societal polarization around diversity, equity, and inclusion (DEI) initiatives, understanding factors that foster progress in DEI is critical. Allyship, the active engagement of advantaged group members in supporting marginalized groups, has emerged as a pivotal component of these efforts. While allyship holds promise in fostering inclusive organizational cultures and promoting diversity, it is a complex, multifaceted process involving three key perspectives: the target (individual or group being supported), the ally (actor engaging in the supportive action), and the observer (third parties impacted by or interpreting the action). Perceptions of allyship and its effectiveness can vary significantly depending on the groups and perspectives involved, underscoring the need to explore the interplay among them. This symposium brings together five empirical papers that advance our understanding of allyship by examining it from all three perspectives. Spanning international contexts, the studies highlight (1) how dominant group members’ recognition of privilege and inequity foster allyship; (2) the effect of allyship actions on marginalized group members' belonging and career outcomes; and (3) how allies are viewed and the downstream consequences of such perceptions. Alyssa Tedder-King, a leading allyship researcher, will provide expert feedback, enhancing discussions for scholars and practitioners aiming to advance DEI initiatives. Examining Caste in America: How Dominance and Marginalization Intersect Among Indians in American Wo Author: Barnini Bhattacharyya; Western University Author: Aparna Joshi; The Pennsylvania State University Author: Sridhar Polineni; University of Michigan Living the Tension? Making Sense of Male Allyship Author: Eugenia Bajet Mestre; University of St. Gallen Author: Mihwa Seong; King's College London Allies in Place of Buddies: Having Supportive Men Matters More for Women When Women Are Underreprese Author: Lillian Kim; New York University Author: Taylor Phillips; New York University Male Allyship: Effects of Communality Perceptions on Men’s Career Outcomes Author: Janice Yue-Yan Lam; York University Author: Ivona Hideg; University of Oxford Author: Janine Bosak; Dublin City University Author: Madeline E. Heilman; New York University Only Time Will Tell: How the Perceived Costs of Allyship Actions Shape the Perceptions of Ally Motiv Author: Juliane Schittek; Imperial College London Author: Celia Moore;
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".