Organizational Narratives to Workplace Inequality
Bibliographic record
Abstract
This symposium aims to deepen and expand our understanding of the relationship between the way organizations and managers understand and provide narratives related to inequality and their subsequent diversity practices. The four papers presented in this symposium offer new theoretical insights and mechanisms for the conditions under which organizations are more (less) apt to redress inequality. This symposium brings together strategic management scholars, economic sociologists, and organizational behavior scholars investigating, with a wide range of methods (qualitative, observational, and experimental), inequality in the workplace. We believe this symposium will generate an insightful discussion and fuel further inquiry into the role organizational narratives play in perpetuating inequality. From Self-Diagnoses to Change: Organizational Narratives and the Gender Pay Gap. Presenter: Mabel Abraham; Columbia Business School Organizational Responses to Diversity Disclosures Presenter: Daphné Baldassari; U. of Toronto Presenter: Sarah Kaplan; U. of Toronto Presenter: Aaron Dhir; York U. What about the Managers? Means-ends decoupling of work policies and the actors liable for their use. Presenter: JoAnne Delfino Wehner; Stanford VMware Women's Leadership Innovation Lab Presenter: Kristine Kilanski; UC San Diego Presenter: Alison Tracy Wynn; Stanford U. Intervening to Advance Equity in Tech Presenter: Robin J. Ely; Harvard Business School Presenter: William A. Kahn; Boston U. Presenter: Sanaz Mobasseri; Boston U. Questrom School of Business
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".