How to Promote Diversity and Inclusion: Learning from Field Data
Bibliographic record
Abstract
This symposium advances knowledge about increasing diversity, equity, and inclusion in organizations by examining data obtained from the field. The first half of the symposium will address factors determining support or decisions around organizational diversity efforts. The latter half will be devoted to field interventions that sought to promote diversity in organizational settings. The first paper investigates how female board directors’ network connectedness shapes the organization’s decisions on increasing future boardroom gender diversity. The second paper investigates the motivations behind the dominant group members’ engagement in allyship behaviors that often belie their own interests. The third paper examines how a 9-week debate training increases Asian employees’ leadership advancement. Finally, using a large-scale field experiment, the fourth paper explores how providing justifications of why one should attend DEI events differentially impact younger versus older individuals’ engagement with these events. The Effects of Female Network Connectedness on Gender Diversity Efforts Author: Jun Lin; Stanford Graduate School of Business Author: Julia D. Hur; New York U. Collective Self-Esteem and Dominant Group Allyship Author: Aastha Chadha; NYU Stern School of Business Author: L Taylor Phillips; NYU Stern Breaking the Bamboo Ceiling and Empowering Asians’ Leadership Advancement with Debate Training Author: Jackson Lu; MIT Sloan School of Management Author: Michelle Zhao; Washington U. in St. Louis Generational differences in responses to diversity rationales Author: Eileen Y. Suh; Boston U. Questrom School of Business Author: Evan P. Apfelbaum; MIT Sloan School of Management Author: Nir Halevy; Stanford 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 | 0.160 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".