Advancing Theory on Gender Dynamics: New Insights from Resource Generation and Utilization Processes
Bibliographic record
Abstract
Although women make up about 47% of the U.S. labor force (U.S. Bureau of Labor Statistics, 2023), there is still a gender gap in diverse contexts. For example, only 2% of venture capital (VC) funding is received by female founders (TechCrunch, 2023), and females remain significantly underrepresented in computer (25%) and engineering (15%) jobs. Scholars have proposed various reasons to explain this gender gap (Woehler, Cullen-Lester, Porter, & Frear, 2021). The first explanation is that gender shapes resource generation: there is a direct gender gap in terms of creating and gaining resources. The second explanation is that gender shapes resource utilization: even when men and women have the same access to resources, they yield different degrees of returns. Thus, our integrative symposium revisits gender differences in resource generation and utilization, contributing to the theoretical development of gender. Building on the two overarching explanations, we bring together five papers that offer new theoretical developments and deepen our understanding of gender in diverse contexts. Specifically, these papers push the knowledge boundaries forward in two dimensions. First, regarding resource utilization, the first half of papers will explore how females, after taking elite positions in universities (e.g., professors) and public firms (e.g., directors), yield different influences on individual careers and firm strategies. Second, concerning resource creation, the second half of the presentations will help us to better understand why the gender gap (or premium) exists in the labor market and entrepreneurship contexts. Gender, Structural Holes, and Citations: The Effects of Women's Increasing Representation Author: Juan Ling; Georgia College & State U. Author: Daniel J Brass; U. of Kentucky Author: Stephen P. Borgatti; U. of Kentucky Author: De Liu; U. of Minnesota Author: Ajay Mehra; U. of Kentucky Gender Bias in Elite Network Diffusion Processes Author: Richard A. Benton; U. of Illinois at Urbana-Champaign Gendered Perceptions of Self-Promotion During Try-out Employment Author: Santiago Campero Molina; U. of Toronto Author: Pablo Escribano; U. Adolfo Ibáñez Author: Massimo Maoret; IESE Business School Author: Lucas Dufour; Toronto Metropolitan U. The Impact of Network Ties on the Gender Gap in Seeking Investment Author: Yajing Li; Alliance Manchester Business School, U. of Manchester Author: Siyu Yu; U. of Michigan Who Benefits Most from Entrepreneurship: Evidence for a Gender Premium to Founding a New Business Author: Aleksandra Joanna Kacperczyk; London Business School Author: Lucia Naldi; Jonkoping International Business School
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".