Understanding the (In)effectiveness of Organizational Diversity, Equity and Inclusion (DEI) Efforts
Bibliographic record
Abstract
Organizations spend annually billons of dollars on DEI-efforts. These efforts, however, are not always effective. Showcasing cutting-edge research, this symposium seeks to answer a critical question for management scholars: Why do many of these costly and often well-intentioned DEI-efforts fail to deliver and what can organizations do about it? The presentations seek to answer this question by illuminating key factors and mechanisms with regards to the (in)effectiveness of DEI-efforts. The presentations are followed by an integrative moderated discussion with a focus on what future research is needed and how the insights from the presentations can be used to better shape DEI-efforts. You can’t fix what you don’t see: Diversity blind spots reduce support for organizational diversity Author: Linda Nguyen; U. of Washington Author: Serena Does; UCLA Anderson School of Management Author: Miguel Unzueta; U. of California, Los Angeles Author: Sapna Cheryan; U. of Washington The Business Case for Diversity Undermines Women’s Performance During Recruitment Author: Oriane Georgeac; Boston U. Questrom School of Business Author: Aneeta Rattan; London Business School A Path Forward for Diversity Training: Bringing More Diversity Science into Practice Author: Ivuoma Ngozi Onyeador; - Author: Hannah McKinney; Boston U. Author: Ashley E. Martin; Stanford Graduate School of Business Gender neutral = Masculine Author: Adriana Germano; Yale School of Management Author: Kristina Olson; Princeton 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 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 | 0.059 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".