Looking Beyond the Numbers: New Approaches to Understanding Organizational Inequality and Exclusion
Bibliographic record
Abstract
In recent decades, organizations big and small, public and private, and across numerous industries have made efforts to increase diversity, equity, and inclusion. At the same time, researchers have documented the many ways in which organizations continue to be fundamentally unequal in their treatment of members from historically underrepresented groups, including women and racial and ethnic minorities. There is a growing consensus among researchers that simply increasing the numbers of members from historically underrepresented groups in organizations (achieving “representational diversity”) will ultimately be insufficient to fundamentally improve equity and inclusion within organizations. This symposium brings together leading scholars of organizational inequality who span micro and macro perspectives to interrogate why simply “adding diversity and stirring” will not be enough to address lasting concerns of organizational equity and inclusion. Specifically, the four papers in this symposium utilize multiple complementary methodologies—ranging from natural language processing of historical texts to attitudinal surveys to video experiments—to shed new light on the mechanisms that continue to uphold inequalities even in the face of changing numeric representation of minoritized groups. The Stability of Stigma: Testing Mechanisms of Negative Stereotype Persistence Across 100 Years Author: Tessa Charlesworth; Northwestern Kellogg School of Management Author: Mark HATZENBUEHLER; Harvard U. Defending White Hegemonic Masculinity: A Test of the Projective Identification Hypothesis Author: Robin J. Ely; Harvard Business School Author: Sanaz Mobasseri; Boston U. Questrom School of Business Author: Ivuoma Ngozi Onyeador; - Demographic Similarity, Protective Socialization: Paradox of Punishment in Racialized Organizations Author: Jayanti Owens; Yale School of Management Race and Evaluations of Misconduct in Organizations Author: Mabel Abraham; Columbia Business School Author: Erica Bailey; Haas School of Business, UC Berkeley
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".