How female entrepreneurs build strong business relationships: the power of gender stereotypes
Bibliographic record
Abstract
Purpose Adopting a feminist constructionist perspective, this article proposes an analysis of the micro-level processes and dynamics of interpersonal, gendered, business relationships between female entrepreneurs, therefore constituting an extension to network theory in the women's entrepreneurship research field. Design/methodology/approach The qualitative research builds on a single, longitudinal case study of a successful, 15-years long collaboration between two female entrepreneurs. Qualitative data were collected over two years, through formal and informal interviews with the entrepreneurs, observations and complementary documentation. The data analysis is based on a grounded theory and narrative approach. Findings The article proposes a thick narrative of the evolution of the dyadic business relationship, and reveals the power of gender role stereotypes in its progressive formation and development. Research limitations/implications The article produces situated knowledge about female entrepreneurs and strong interpersonal business ties. The limitations relate to the specificity of the case analysed, representing the viewpoint of privileged, white, Western, educated and wealthy female entrepreneurs. It therefore does not account for the diversity of women's entrepreneurship. Originality/value The article enriches and extends social network theory in the women's entrepreneurship field through analysing how gender is done in discursive and social practices at the interpersonal level. The case also constitutes an illustration of social feminism in women's entrepreneurial practice, challenging dominant gender stereotypes.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| 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".