Organizational Practices and Labor Market Inequality
Bibliographic record
Abstract
This symposium investigates how organizational structures and practices shape labor market inequalities, featuring four cutting-edge studies that illuminate the mechanisms fostering or hindering inclusion. It offers two unique contributions to this critical conversation. First, the papers explore a range of mechanisms—including applicant interviews, referral-based hiring, task allocation, and promotion systems—that directly affect career trajectories for members of URGs. By uncovering these processes, the studies offer actionable strategies for recalibrating organizational practices to achieve more equitable outcomes. Second, the papers draw on a diverse array of data sources and empirical settings, ranging from startups to established firms and from academic job seekers to corporate leadership, leveraging both quantitative and qualitative methods. This breadth ensures a nuanced and comprehensive understanding of how organizational practices operate in varied contexts. Do Employers Change their Recruitment Choices Based on Past Interview Outcomes Author: Santiago Campero Molina; University of Toronto Strength in Numbers: Under-Represented Groups, Social Capital and Getting a Referral in Big Tech Author: Elena Obukhova; McGill University Author: Sharon Koppman; University of California Irvine Belonging in the Balance: Networks, Demographics, and the Making of Organizational Identity Author: Brandy Aven; Author: Evelyn Ying Zhang; Nanyang Technological University Premium or Penalty? Differential Effects of Gender and Race on Internal Promotions to Top Management Author: Tiantian Yang; University of Pennsylvania
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 | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".