Richness in diversity: Towards more contemporary research conceptualisations of women’s entrepreneurship
Bibliographic record
Abstract
Analyses of the diversity of women entrepreneurs and their enterprises, using novel approaches and theoretical viewpoints, is lacking in contemporary scholarship. Accordingly, this article reviews and critiques five articles that constitute this Special Issue (SI) focused on exploring the diversity of women’s entrepreneurship. The authors acknowledge that entrepreneurship is a rich and multi-coloured tapestry, hence, these SI articles highlight the complexities of women entrepreneurs and celebrate their diversity through signposting towards research conceptualisations that reflect the actual rather than the assumed status quo. The article contributes to extant scholarship by platforming the heterogeneity of women’s entrepreneurial endeavours, supporting the view that in terms of supporting women’s entrepreneurship, ‘one size (still) does not fit all’. We also propose a framework to help future scholars strengthen the quality and relevance of their research on women entrepreneurs along four key dimensions: influence of context; theoretical development; multiplicity of dimensions; and heterogeneity.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".