The Promises and Pitfalls of Diversity and Inclusion in Organizations and Society
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.008 |
| 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 it