Perceptions of & Engagement in Allyship: A Multiple Perspective Approach
Bibliographic record
Abstract
This symposium addresses two overarching topics: the perceptions of allyship actions and allies across social identity groups (presentations 1, 2 & 3), and ways to increase allies’ and their actions’ effectiveness (presentations 4 & 5). Specifically, the first two presentations elaborate the specific behaviors that represent allyship, and provide evidence of a miscalibration of how these actions are perceived depending on one’s social identity groups. The third presentation focuses on how allies who are selectively silent (speak out about some social issues but not others) are perceived, compared to those choose to remain completely silent. The second part of the symposium considers allies’ perspectives and identifies ways to increase their actions’ effectiveness. The fourth presentation offers qualitative insights into how allies can remain committed to their allyship in the face personal discomfort, and the final presentation highlights the importance of male advocates to allyship effectiveness. Taken together, this symposium advances our understanding of allyship and offers nuanced views on how to be better, more effective allies. Actioning Allyship: What Makes Acts of Allyship Effective and for Whom? Author: Juliane Schittek; Imperial College Business School Author: Celia Moore; Imperial College Business School When (Selective) Silence is Violence Author: Kaylene McClanahan; U. of California, Los Angeles Author: Hannah Birnbaum; Washington U. in St. Louis, Olin Business School Author: Miguel Unzueta; U. of California, Los Angeles Fine Lines and Dances: Understanding (and Overcoming) the Challenges of Allyship at Work Author: Olivia Foster-Gimbel; NYU Stern School of Business Advocating for Female Leaders: The Role of Positive Stereotypes and Male Allies Author: Michelle Checketts; U. of Illinois at Urbana-Champaign Author: Taeya Howell; Brigham Young U. Author: Denise Lewin Loyd; U. of Illinois at Urbana-Champaign Author: Emily T. Amanatullah; Georgetown U. Author: Catherine Tinsley; Georgetown U., McDonough School of Business Advantaged Groups Misperceive how Allyship Will Be Received Author: Hannah Birnbaum; Washington U. in St. Louis, Olin Business School Author: Desman Wilson; Northwestern Kellogg School of Management Author: Adam Waytz; Northwestern Kellogg School of Management
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".