Standing on the shoulders of giantesses: how women technology founders use single and mixed gender networks for success and change
Bibliographic record
Abstract
Purpose Building on research about entrepreneurship and social capital, the purpose of this paper is to explore how women founders of technology-based ventures in Canada access and use formal external entrepreneurial networks to build their companies. Design/methodology/approach The study draws on 25 semi-structured interviews with women founders of technology firms and leaders of formal networks. Findings The authors demonstrate the positive impact of women only networks (WON) for founders including increasing entrepreneurial diversity, access to financing, and founder credibility and sponsorship. The authors show how women founders use mixed gender and WON to build their businesses and conclude that membership in WON can be a vital step. Research limitations/implications The sample size is small and most participants reside in highly urban areas, which may limit generalizability. Findings may not generalize beyond Canada due to cultural and structural differences. Practical implications The research suggests that external WON should be encouraged as important resources for founder identity work which may enable positive change. Social implications This research can assist in designing initiatives that support women entrepreneurs and promote gender parity. Originality/value The authors draw on research in women's leadership development to explain how WONs for entrepreneurs help founders create overlapping strategic networks – a unique form of social capital – and serve as identity workspaces for the identity work women founders must complete. The authors argue that the identity work in WONs can be a mechanism by which gender structures are challenged and eventually changed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".