Recognizing, Measuring, and Addressing Racial Biases in Organizations
Bibliographic record
Abstract
Institutions around the world are focusing increasingly on issues of diversity, equity, and inclusion (DEI), not just to improve organizational performance, but also as a means of delivering positive societal impacts by ameliorating barriers and providing opportunities to members of hitherto marginalized groups (DEI, 2022a; Gabriel et al., 2022; Roberson, 2019, 2022). And while the management field has made progress in advancing our understanding of DEI-related issues, much more is needed if we are to effectively answer this “grand challenge” (Benschop, 2021; George et al., 2016; Hideg et al., 2020). This presenter symposium presents novel approaches to help advance management research and practice by recognizing, measuring, and addressing racial biases in organizations. Together, the five papers identify how racial biases continue to influence business schools today (Ramani, McGinley, & Desir), how intersectionality of gender and racial stereotypes about Asians affects organizational employees (Khatija & Bilimoria), and in particular female professors (Radhakrishnan, Romain, Thavarajah, & Huang), how subtle acts of racial discrimination (i.e., microaggressions) are enacted in the workplace (Harmata), and how employees adopt particular behaviors in response to these discriminatory acts (Lumbreras, Melson-Silimon, Robertson, & Outland). Overall, the symposium provides new perspectives on how racial and gender biases affect minority employees, and offers recommendations for future research as well as practical advice for organizations seeking to address such issues. Recognizing, Measuring, and Addressing Racial Biases in Organizations Author: Ravi Ramani; Morgan State U.
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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 | 0.052 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".