The Promises and Pitfalls of Diversity and Inclusion in Organizations and Society
Bibliographic record
Abstract
Despite efforts to diversify organizations and move toward a more antiracist society, progress toward these noble goals remains elusive and, often, misunderstood (Dobbin, Kim, & Kalev, 2011; Kraus, Rucker, & Richeson, 2017; Opie & Roberts, 2017; Williams, 2001). Historically underrepresented groups still struggle to integrate into, persist in, and feel included within organizational cultures (Acker, 2006; Ray, 2019; Rivera & Tilcsik, 2016). Corporate leaders continue to make racially biased statements, which observers can meet with intolerance and indignance. Furthermore, despite the amount of time, resources, and money put forth to create a more inclusive workplace and world, there is a lack of understanding surrounding what strategies are effective and why (Kalev, Dobbin, & Kelly, 2006). Considering the current dynamics of our society, organizational leaders are in need of consensus to create effective and sustainable diversity, equity, and inclusion initiatives. In this symposium, we consider the challenges, constraints, and possibilities within efforts to create a more diverse and inclusive society. We hope to suggest practical pathways forward to engage with and bring managers back into these conversations. What organizational policies and practices impede progress toward diversity and inclusion of underrepresented groups, such as women and racial minorities? What psychological processes predict whether onlookers respond to the prejudiced corporate leader with intolerance or forgiveness? How are diversity and inclusion personnel, particularly workers of color, enabled and constrained in their ability to improve the climate of a large-scale organization such as a university? These are a few of the questions our presenters will answer in this symposium on the promises and pitfalls of diversity and inclusion in organizations and society. Racialized Expertise and the Enabling and Constraining Character of Organizations Presenter: Sandra Portocarrero; Columbia U. Maternity Salience: How Concerns About Women’s Family Demands Undermine Fair Treatment Presenter: Ezgi Ozgumus; London Business School Presenter: Aneeta Rattan; London Business School Bias Intolerance: Predicting Condemnation of Apologetic Perpetrators of Prejudice Presenter: Ivuoma Onyeador; Northwestern Kellogg School of Management Presenter: Rebecca Neel; U. of California, Riverside - Anderson Graduate School of Management Presenter: Bethany Lassetter; U. of Toronto Presenter: Andre Wang; U. of California, Davis Presenter: Andrew Todd; U. of California, Davis Presenter: Jenessa Shapiro; U. of California, Los Angeles The Misperception of Progress Toward Diversity, Equity, and Inclusion in Organizations Presenter: Brittany Torrez; Yale U. Presenter: LaStarr Hollie; Yale School of Management Presenter: Michael W. Kraus; Yale School of Management
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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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.070 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".