Women’s entrepreneurship policy: a 13 nation cross-country comparison
Bibliographic record
Abstract
Purpose Government attention to women’s entrepreneurship has increased in the past two decades; however, there are few cross-cultural studies to inform policy development. This paper aims to draw on gender and institutional theory to report on the status of female-focused small and medium-sized enterprises/entrepreneurship policies and to ask how – and to what extent – do women’s entrepreneurship policies differ among countries? Design/methodology/approach A common methodological approach is used to identify gaps in the policy-practice nexus. Findings The study highlights countries where policy is weak but practice is strong, and vice versa. Research limitations/implications The study’s data were restricted to policy documents and observations of practices and initiatives on the ground. Practical implications The findings have implications for policy makers in respect of support for women’s entrepreneurship. Recommendations for future research are advanced. Originality/value The paper contributes to extant knowledge and understanding about entrepreneurship policy, specifically in relation to women’s entrepreneurship. It is also one of the few studies to use a common methodological approach to explore and compare women’s entrepreneurship policies in 13 countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".