Re-thinking White Standards of Leadership to Develop Women, Black, South and East-Asian Leaders
Bibliographic record
Abstract
This symposium showcases a set of four papers that adopt innovative approaches to uncover the inherent biases in describing and assessing women and racial minority leaders. The perspectives that the papers bring fill important gaps in existing literature by demonstrating surprising ways in which bias against women and racial minority leaders are external to the organization can also be perpetuated within it. They illustrate the way Asian CEOS are stereotypically depicted in social media, how previously established and validated personality assessments are unreliable in women and racial minority populations, how unconscious preferences for White standards of leadership and physical attributes (e.g., facial proportions) work against women, South-Asian, East-Asian, and Black leaders in how likely they are seen to be leader-like, and in how a group of Black women experience bias in a predominantly white corporate environment. The papers are novel in their methodology, use a variety of data sources (media narratives, field studies, experiments, and interviews) and examine leadership in different spheres (both politics and business) and across organizational ranks. They reveal the importance of assuming that women and racial minorities, or that their intersectional identities are an undifferentiated whole. They illustrate how research and practice in human resources should include diverse “others” when developing them as leaders in the organization. The chair and discussant will lead in a nuanced discussion of how researchers should examine leadership in these women and racial minority populations and how organizations should recruit select, promote, and retain a multi-racial workforce. Facial Cues or Racial Hues? The Role of Face Ratio and Racial Bias in Perceptions of Leadership Presenter: Pankaj Aggarwal; U. of Toronto Presenter: Ahreum Maeng; U. of Kansas Conforming yet countering: Navigating Gender and Racial Stereotypes while Becoming a Leader Presenter: Phanikiran Radhakrishnan; U. of Toronto at Scarborough Presenter: Jaffa Romain; U. of Toronto South Asian and East Asian CEO Media Narratives: A look at the Intersectionality of Race and Gender Presenter: Pooja Khatija; Organizational Behavior Case Western Reserve U. African American Experiences of Unfairness in the Corporate Work Environment Presenter: Alecia Bracy; Capella U.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".