DEI Practices in Organizations: Effectiveness, Impact, and Unintended Consequences
Bibliographic record
Abstract
This symposium examines the complex and often contradictory nature of addressing workplace inequities through diversity initiatives. Organizations are increasingly investing in diversity and inclusion (D&I) efforts, as evidenced by the prevalence of Chief Diversity Officers and comprehensive diversity training programs among Fortune 1000 companies. These efforts represent a deliberate strategy to cultivate equitable and inclusive workplaces. However, the symposium emphasizes the necessity of evaluating the impact of these practices to ensure they contribute to real and sustainable change, rather than being mere symbolic gestures. The studies featured explore the complex dynamics of workplace inequities and the often paradoxical outcomes of well-intentioned D&I efforts. These studies employ various methods such as surveys, audit studies, and experiments to assess organizational policies across different contexts. Key findings include: 1) Gender differences in EEOC judgments of merit, with claims filed by women in masculine industries more likely to be granted merit. 2) The impact of free speech appeals on reducing accountability for workplace bias, highlighting a failure in achieving D&I goals. 3) The concept of strategic ignorance in sexual harassment claims, indicating that claims of ignorance may not always be made in good faith. 4) The effectiveness of positive versus negative feedback in motivating equitable behaviors among city councilors. 5) The exploitation of first-generation college students in organizations due to positive stereotypes. These studies collectively reveal the intricate nature of addressing workplace inequities, underscoring the need for more nuanced and effective strategies in fostering true equity and fairness in the workplace. He Said She Said: How Gender Relates to Judgments about the Merit of Workplace Accusations Author: Grusha Agarwal; U. of Toronto, Rotman School of Management Author: Chloe Kovacheff; U. of Toronto Author: Rachel Lise Ruttan; U. of Toronto Author: Gabrielle Adams; U. of Virginia Darden School of Business Author: Katherine Ann DeCelles; U. of Toronto What About My Free Speech? Appeals to Free Speech Reduce Accountability for Workplace Bias Author: Ivuoma Ngozi Onyeador; - Author: Felix Danbold; UCL School of Management Playing Dumb: Strategic Ignorance about what Constitutes Sexual Harassment Author: Natalya Alonso; Beedie School of Business Simon Fraser U. Author: Zhanna Lyubykh; Beedie School of Business Simon Fraser U. Author: Sandy Hershcovis; U. of Calgary What motivates equitable behavior? The effects of positive & negative feedback in the domain of bias Author: Erika Kirgios; U. of Chicago Booth School of business Author: Edward Chang; Harvard Business School The Heroization and Exploitation of First-Generation College Students Author: Shuang Wu; Rady School of Management, U. of California San Diego Author: Peter Belmi; U. of Virginia
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 | 0.039 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".