Beyond Structure: How Men and Women Perceive, Experience, and Utilize Their Professional Networks
Bibliographic record
Abstract
Social and professional networks are consequential for professional success because who individuals are connected to determines access to valuable resources and opportunities that facilitate career advancement. Problematically, there is evidence of gender differences in networks, which are thought to be a contributing factor to persistent gender gaps in the workplace. Evidence of inequality in the returns men and women experience even from equivalent network structures underscores the importance of going beyond structure to understand how men’s and women’s networks impact their experiences at work and their career trajectories. This symposium contributes to this growing area of research by bringing together four projects that speak to gender differences in how men and women perceive, experience, and utilize their social and professional networks. Who do men and women go to for help at work? Advice network homophily and task-level organization Presenter: Roman V. Galperin; McGill U. - Desautels Faculty of Management Presenter: Jennifer M. Merluzzi; George Washington U. The impact of a sponsor’s goal: Gender, goals, and cognitive network activation in sponsorship Presenter: Elizabeth Lauren Campbell; Rady School of Management, U. of California San Diego Presenter: Catherine Shea; Carnegie Mellon U. - Tepper School of Business Calling on ties when everyone is under siege: Gender, pervasive threat, and network utilization Presenter: Kristin Cullen-Lester; U. of Mississippi Presenter: Meredith Lauren Woehler; Purdue U. Presenter: Houston Floyd Lester; U. of Mississippi Presenter: Pol Solanelles; U. of Mississippi Unintended consequences: #MeToo, salience of accusations, and women's exclusion from networks Presenter: Ren Li; The Hong Kong Polytechnic U. Presenter: Kaylene McClanahan; U. of California, Los Angeles Presenter: Raina A. Brands; UCL School of Management
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| 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".