Racial Segregation at Work and Economic Outcomes: Causes and Consequences of Organizational Sorting
Bibliographic record
Abstract
This symposium will assemble novel research on the causes and effects of between-organization racial segregation. One set of presentations complicates our theories about the mechanisms underlying racial segregation in organizations and markets. Another set of presentations contributes to our understanding of what sorting means for the economic outcomes of racial minorities and the composition of high-status occupations. These findings supply scope conditions, new mechanisms, and new results for previous theories of racial sorting. Organizational Diversity Rationales and Job Seeker Behavior Author: Jordan Starck; Stanford University Author: Janet Xu; Stanford University Racial Segregation in Informal Networks—The Structural Limits of Homophily in Entrepreneurial Market Author: Victoria Zhang; Massachusetts Institute of Technology Firm Segregation and the Structure of Racial Earnings Inequality Author: Clem Aeppli; Harvard University Black Success in a Predominantly White Industry: The Role of Marginalized Professional Capital Author: Alicia Sheares; Stanford University
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".