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Re-thinking White Standards of Leadership to Develop Women, Black, South and East-Asian Leaders

2022· article· en· W4286622605 on OpenAlexaffabout
Soo Min Toh, Devyani Mahajan, Pankaj Aggarwal, Alecia Bracy, Pooja Khatija, Phanikiran Radhakrishnan, Ahreum Maeng, Jaffa Romain

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsWorkforcePublic relationsWhite (mutation)Political scienceGender studiesLeadership developmentNarrativeSociologySocial psychologyPsychology

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.145
GPT teacher head0.307
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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