Unravelling Workplace Inequality: The Role and Effectiveness of Organizational Diversity Practices
Bibliographic record
Abstract
Despite organizations’ efforts to improve diversity and inclusion at work, many diversity practices have yielded limited impact, while some lead to unintended consequences. While existing research has primarily focused on assessing support for these practices, there remains a critical gap in understanding the contextual nuances influencing their impact. To bridge this gap, this symposium aims to investigate organizational practices promoting diversity. Across five papers, this symposium (1) addresses the limitations of current diversity practices and whom they work best for, (2) proposes practical and innovative interventions to reduce biases and increase representation during recruitment and hiring, and (3) explores causes for why DEI supporters may become opponents to safeguard against setbacks. Taken together, this symposium provides insights for organizations to consider when adopting diversity practices. Who Fits This Goal? Intersectional Effects on Selection Decisions Under Vague and Specific Diversity Author: Ivy Mai; U. of Calgary Author: Natalya Alonso; Beedie School of Business Simon Fraser U. Author: Justin Weinhardt; U. of Calgary The Limits of Technical Fixes: Female Medical Students Make More Mistakes in the US Residency Match Author: Samuel Skowronek; UCLA Anderson School of Management Author: Joyce He; U. of California, Los Angeles Reducing Discrimination Against Individuals With Mental Impairments: The Influence of Section 503 Author: Christine Nittrouer; Texas Tech U. Author: Naomi Fa-Kaji; U. of Virginia Author: Michelle Hebl; Rice U. A Longer List of Referrals Increases Gender Diversity: Evidence From Two Field Experiments Author: Aneesh Rai; U. of Maryland R.H. Smith School of Business Author: Erika Kirgios; U. of Chicago Booth School of business Author: Brian J. Lucas; Cornell U. Author: Katherine Milkman; U. of Pennsylvania DEI U-Turns: When and Why DEI Supporters Become Opponents Author: Camellia Bryan; Schulich School of Business Author: Felix Danbold; UCL 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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".