Tipping Points: The Gender Segregating and Desegregating Effects of Network Recruitment
Bibliographic record
Abstract
Current scholarship commonly posits that network recruitment contributes to job sex segregation and that the segregated nature of personal contact networks explains this effect. A variety of empirical findings inconsistent with this explanation demonstrate its inadequacy. Building on Kanter’s observation that recruitment processes often resemble “homosocial reproduction” [Kanter RM (1977) Men and Women of the Corporation (Basic Books, New York)], we develop a population dynamics model of network recruitment. The resulting formal model builds a parsimonious theory regarding the segregating effects of network recruitment, resolving the puzzles and inconsistencies revealed by recent empirical findings. This revised theory also challenges conventional understandings of how network recruitment segregates: in isolation, network recruitment—even with segregated networks—is more likely to desegregate rather than segregate. Network recruitment segregates primarily through its interactions with other supply-side (e.g., gendered self-sorting) or demand-side (e.g., gendered referring rates) biasing mechanisms. Our model reveals whether and to what extent network recruitment segregates or desegregates, and it reveals opportunities for organizational intervention. There is an easily calculable tipping point where demand-side factors such as gender differences in referring can counteract and neutralize other segregating effects from referring. Independent of other personnel practices, organizational policies affecting employees’ referring behaviors can tip the balance to determine whether network recruitment serves as a segregating or desegregating force. We ground our model empirically using three organizational cases.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".