Tipping Points: The Gender Segregating and Desegregating Effects of Network Recruitment
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it