The Confluence of Gender and Entrepreneurship: Moving Research Forward by Embracing Parallel Research Streams
Bibliographic record
Abstract
This paper aims to synthesize the gap between two traditionally separate fields, gender and entrepreneurship, by creating a coherent and integrated program of research that explores the intersection of these domains. Through a comprehensive literature review, we identify themes that connect these fields and inform future scholarship. Our findings reveal that systemic biases within the entrepreneurship ecosystem, rather than individual deficiencies, perpetuate discrimination and inequalities against women. The focus has been unfairly placed on women to ‘fix’ themselves rather than addressing structural barriers. Additionally, we highlight the need for theoretical and methodological shifts, as the current reliance on feminist empiricism, postfeminism, and quantitative research provides an incomplete picture. Poststructural feminism and qualitative approaches are necessary to capture the full complexity of gender dynamics in entrepreneurship. From a practical perspective, this research offers actionable insights for both entrepreneurship educators and small business consultants. Educators are encouraged to revise curricula to integrate gender-sensitive frameworks, adopt inclusive teaching methods, and promote critical discussions on structural inequities. Small business consultants are urged to challenge biases in advisory practices, tailor their guidance to address gender-specific challenges, and advocate for systemic policy changes that promote inclusivity. By addressing these practical implications, stakeholders can contribute to the creation of a more equitable entrepreneurial ecosystem. This paper is among the first to systematically integrate insights from both gender and entrepreneurship research, offering actionable strategies and new directions for both scholarship and practice.
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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 | 0.084 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.029 | 0.075 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".