When and How Are Allies Promoters of Social Change? An Examination of Allyship in the Workplace
Bibliographic record
Abstract
Allies have played instrumental roles in efforts to address inequity in society. In the wake of civil unrest triggered by the shooting deaths of unarmed Black people, the brutalization of Asian Americans during the COVID-19 pandemic, and state legislatures passing laws that seek to reduce voting and women’s rights, the supportive action taken by allies is gaining attention once again. This year’s Academy of Management Conference theme “Creating a Better World Together” is a call to action for scholarship that provides insight into how organizations can anticipate and solve these important societal challenges. Yet, despite management scholars suggesting allyship is an effective way to create organizational change, the literature on allyship is still quite scarce. This symposium includes a collection of papers that examines open research questions pertaining to allyship including: who or what entities are able to engage in allyship behavior, why those actors choose allyship, and when (i.e., which contexts) support allyship. Following the presentations, Denise Lewin Loyd, a major contributor to research in the field of diversity and inclusion, will serve as our symposium’s discussant. Together, we hope that this symposium will provide important insights into how to create meaningful change in organizations through allyship. LEAP at Work: Examining the Effects of Race-Based Allyship Training in the Workplace Presenter: Stephanie J. Creary; The Wharton School, U. of Pennsylvania Presenter: Tianna Shari' Barnes; U. of Pennsylvania Presenter: Ozias Moore; Lehigh U. Institutional Allyship, a Ritual of Recovery Presenter: Tiffany Dawn Johnson; Georgia Institute of Technology Presenter: Juanita Kimiyo Forrester; Mercer U. Presenter: Natasha Denise Reed; Georgia Institute of Technology Bursting the Bubble of Performative Allyship: How Moral Performance Compromises Inter-Group Learning Presenter: Karren Kimberly Knowlton; Tuck School of Business at Dartmouth Presenter: Rachel Arnett; The Wharton School, U. of Pennsylvania Who’s Expected to be an Ally? An Examination of Allyship and Leadership Evaluations Presenter: McKenzie Preston; The Wharton School, U. of Pennsylvania Presenter: Angelica Leigh; Fuqua School of Business, Duke U. Presenter: Terrance L. Boyd; Louisiana State U. Presenter: Richard Burgess; U. of North Carolina, Chapel Hill Presenter: Vic Marsh; U. of Toronto, Rotman School of Management Practice What You Preach: Performative Allyship in Organizations’ Support for Racial Equity Presenter: Roxanne Ross; James Madison U. Presenter: Horatio Traylor; U. of Houston Presenter: Enrica Nicole Ruggs; U. of Houston
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".