Diversity, Equity, and Inclusion Practices: Unveiling the Unforeseen Outcomes
Bibliographic record
Abstract
Promoting Diversity, Equity, and Inclusion (DEI) is crucial for organizations, yet these efforts can inadvertently lead to negative consequences for women and racial minorities. This symposium aims to delve into the underlying mechanisms of these unintended outcomes and highlight effective solutions. By inviting leading scholars in this domain to share ongoing research, this platform seeks to dissect the complexities of DEI implementation, paving the way for more nuanced strategies. It also aims to identify proactive measures to minimize negative impacts and amplify positive outcomes. With a forward-looking approach, this symposium endeavors to shape the future trajectory of DEI research and practice, offering valuable insights for fostering inclusive and equitable organizational environments. Gender differences in perceptions of meritocracy Author: Shoshana Schwartz; Christopher Newport U. Author: Isabel Fernandez-Mateo; London Business School Author: Herminia Ibarra; London Business School Author: Dana Kanze; London Business School Parental Leaves and Men’s Communality Advantage at Work Author: Anja Krstic; York U., Toronto Caught Between Female Tokenism And Female Dominance Author: Anthea (Yan) Zhang; Rice U. The Gendering of Job Application Sources: Analyzing Hiring Outcomes across Job Titles, Organizations Author: Emilio J. Castilla; MIT Sloan School of Management Author: Francesco Sguera; UCP - Católica Lisbon School of Business & Economics
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.021 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".