New Perspectives on Increasing Diversity, Equity, and Inclusion
Bibliographic record
Abstract
Organizations around the world have rapidly increased the resources they are devoting to improving diversity, equity, and inclusion (DEI) over the past several years. However, these initiatives have fallen short. Women and people of color remain underrepresented in high-status jobs and often do not feel included in the workplace. Across five presentations, this symposium brings together papers that focus on two components of DEI: 1) decisions in the hiring process that could increase racial and gender diversity of organizations, and 2) decisions in organizational structures that can impact equity and inclusion in organizations. This symposium features papers that utilize a wide range of methods – ethnographic work, incentivized behavioral experiments, field experiments, archival data analysis, and more – to provide insights in this key area. Following the presentations, Dr. Laura Kray, a scholar with innovative and impactful work on the social psychological barriers that influence inequality in organizations, will facilitate a discussion about the papers and future research on advancing diversity, equity, and inclusion in organizations. Evaluating the efficacy of the Rooney Rule in promoting gender diversity Author: Linda Chang; The Wharton School, U. of Pennsylvania Author: Erika Kirgios; U. of Chicago Booth School of business Author: Aneesh Rai; The Wharton School, U. of Pennsylvania Does challenging women to close the gender gap in competitiveness change their behavior? Author: Sophia Pink; The Wharton School, U. of Pennsylvania Author: Jose Cervantez; The Wharton School, U. of Pennsylvania Author: Edward Chang; Harvard Business School Author: Katherine Milkman; U. of Pennsylvania Naming and framing of minority racial labels in formal disclosure Author: Grusha Agarwal; U. of Toronto, Rotman School of Management Author: Joyce He; U. of California, Los Angeles Author: Sonia Kang; U. of Toronto Unpacking how organizational values and incentives reinforce gendered career support processes Author: Elizabeth Lauren Campbell; Rady School of Management, U. of California San Diego Feminine defaults are associated with a reduction in the gender participation gap in MBA classrooms Author: Mindy Truong; Northwestern Kellogg School of Management Author: Hannah Birnbaum; Washington U. in St. Louis, Olin Business School Author: Andrea Dittmann; Emory U., Goizueta Business School Author: Nicole Stephens; Northwestern U. Author: Sarah S M Townsend; U. of Southern California Author: Lydia Emery; Northwestern Kellogg School of Management Author: Rebecca Carey; Princeton U.
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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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".