Challenges and Opportunities for Organizations to Increase Representation
Bibliographic record
Abstract
Despite widespread recognition of its importance, numerous organizations still grapple with effectively implementing genuine diversity and inclusion (McKinsey, 2020). This discrepancy between aspiration and reality poses a pivotal challenge: How can organizations innovatively address contemporary challenges to enhance representation? Our sessions aim to dissect both the challenges and potential solutions for cultivating more representative environments at various stages of the organizational pipeline. Specifically, we will demonstrate diverse methods by which organizations can boost representation, ranging from harnessing feedback mechanisms to modifying organizational communication, identifying gaps in the pipeline stages, and understanding mentorship dynamics among individuals from working-class backgrounds. Additionally, our symposium features a diverse group of researchers, offering insights into how representation can permeate through the research process itself. The Role of Feedback in Promoting Diversity Author: Jose Cervantez; The Wharton School, U. of Pennsylvania Author: Sophia Pink; The Wharton School, U. of Pennsylvania Author: Katherine Milkman; U. of Pennsylvania Author: Aneesh Rai; U. of Maryland R.H. Smith School of Business Author: Linda Chang; The Wharton School, U. of Pennsylvania Author: Mohsen Mosleh; MIT Sloan School of Management The Gender License Gap: Gendered Barriers in the Engineering Licensing Process Author: Joyce He; U. of California, Los Angeles Author: Sonia Kang; U. of Toronto Communicating Commitment to Gender Equality in Organizations Author: Elizabeth Huppert; Northwestern Kellogg School of Management Author: Maryam Kouchaki; Northwestern Kellogg School of Management Mentorship for Whom? Relational Mentorship Frames Reduce Social Class Gaps in Ment Author: Kathy Vo; Kellogg School of Management, Northwestern U. Author: Andrea Dittmann; U. of Southern California - Marshall School of Business Community First: Promoting Aid Access with an Interdependent Model of Agency Author: Ilana Brody; UCLA Anderson School of Management Author: Sherry Wu; UCLA Anderson School of Management Author: Eugene M. Caruso; UCLA Anderson School of Management Author: Heather M. Caruso; UCLA Anderson 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 | 0.101 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.028 | 0.024 |
| Open science | 0.005 | 0.042 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".