Drivers of Racial and Gender Workplace Inequalities
Bibliographic record
Abstract
This symposium focuses on the drivers of workplace inequality. Racial and gender inequalities are highly persistent in hiring and participation in the workplace. Past research shows that workplace inequalities are driven by two types of mechanisms: demand-side and supply-side mechanisms. In this symposium, we put together five papers that provide insights into how organizations and external stakeholders (e.g., labor market intermediaries) can mitigate or exacerbate these inequality drivers. Each paper investigates inequality mechanisms through either a supply- or a demand-side lens, examining multiple stages in the organization: applying, hiring, and contributing. Considered together, these papers shed light on how organizations can jointly think of supply- and demand-side factors when designing their hiring and knowledge contribution processes. Exploring the Differences in Gender-based Evaluations by Intermediaries versus Hiring Firms Author: Xuege (Cathy) Lu; U. of Minnesota Carlson School of Management Author: Halil Sabanci; Frankfurt School of Finance & Management Author: Elizabeth McClean; Cornell SC Johnson College of Business Race Composition of the Applicant Pool and Employers’ Decision Not To Hire Author: Santiago Campero Molina; U. of Toronto Gender Differences in the Use of Recommendation letters in the Job Search Process Author: Kira Choi; EMLYON Business School The Role of Online Socialization at the Workplace: Impact on Reducing Gender Disparity Author: Jason Chan; - Author: Christina Yong Jeong; U. of Minnesota Author: Yue Guo; southern U. of science and technology How Social Movements Influence Hiring via Networks: Evidence from the Film Industry Author: Daphné Baldassari; U. of Toronto, Rotman School of Management
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".