Scouting and Schmoozing: A Gender Difference in Networking during Job Search
Bibliographic record
Abstract
Networking behaviors are a potentially important factor driving gender differences in social networks and contributing to the gender gap in career achievement, yet we know little about how and why gender shapes networking behavior. To fill this gap, we study the networking behavior of female and male job-seekers in a strategic research setting. In Study 1, we use server logs to directly observe job-seeking MBAs students’ outreach to alumni and show that female students reach out to at least as many men and to significantly more women than their male classmates. In Study 2, 46 interviews reveal that in addition to all the same networking that men do, female job-seekers also networked, primarily with women, to assess two aspects of fit that men were less concerned about: gender dynamics and support for parenting. Taken together, our results suggest that both genders engage in schmoozing to get a job, but women also engage in scouting – a heretofore undocumented form of gender-homophilous networking aimed at finding employers and career options that give women a fair chance at professional success. We discuss the implications of our findings for our understanding of gender differences in networks and career attainment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".