Diversity Dilemmas: Examining the Antecedents and Aftermath of Pro-DEI Behaviors
Bibliographic record
Abstract
Despite support for diversity, equity, and inclusion for both moral and financial reasons, only 12% of Fortune 100 CEOs are women, and only 14% are non-White (Heidrick & Struggles, 2022). Even in 2017, 42% of women say they’ve faced gender discrimination at work, and in 2020 about 25% of Black and Hispanic individuals reported experiencing discrimination in their workplace (Reiners, 2022). As a society, we have a long way to go in order to understand why our pro-DEI beliefs fail to line up with the current reality of our organizational world, and to ensure greater efficacy in our practices around diversity, equity, and inclusion. In this symposium, we offer five presentations that provide empirical tests of the antecedents (Presentations 1 & 2) and consequences (Presentations 3, 4 & 5) of pro-DEI behavior in organizations. This work builds upon existing theoretical frameworks on the antecedents (e.g., motivations; Radke et al., 2020) and consequences (e.g., impact; Selvanathan, Lickel, & Dasgupta, 2020) of pro-DEI behaviors, while empirically adding to our knowledge in several organizational literatures including those on motivation, hierarchy maintenance, and intersectionality. In a world where both individuals and organizations routinely engage in behavior to promote diversity, equity, and inclusion, our hope is that this collective work will illuminate both the antecedents and consequences of these pro-DEI behaviors. Demographic Characteristics Shape Perceptions of Diversity Expertise Author: Rebecca Ponce de Leon; Columbia Business School Author: James T. Carter; Columbia Business School Friend or Faux: Performative Wokeness and Signaling One's Awareness of Social Issues Author: Preeti Vani; Stanford Graduate School of Business Author: Peter Belmi; U. of Virginia Author: Gabrielle Adams; U. of Virginia Employee Perceptions of White and Racial Minority Leaders who Remain Silent on Racial Equity Issues Author: McKenzie Preston; The Wharton School, U. of Pennsylvania Author: Richard Burgess; U. of Pittsburgh How Organizational Data Analysis Practices Conceal Racialized Gender Differences in Belonging Author: Ezgi Ozgumus; London Business School Faculty Evaluations in the Age of COVID: Evidence from the Field Author: Lauren A. Rivera; Northwestern Kellogg 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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".