Gender inclusive entrepreneurship education and training: challenges and indicators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Globally women are under-represented in entrepreneurship education and training (EET) programmes both in universities and in communities. At the same time, content, delivery and evaluation practices often normalize the ideal male entrepreneur from the Global North, overlooking women or gender diverse entrepreneurs in university and community-based EET programmes. To inform pedagogy, this study poses two questions: 1) What are the challenges that limit the enrolment and participation of gender diverse learners in EET programmes? and 2) What are the indicators that characterize inclusive EET programmes? Drawing on the expertise of entrepreneurship educators from 19 countries, a Delphi panel study (n = 85) was employed to reduce and refine a pool of 35 indicators that characterize gender inclusive EET. Supporting a social feminist perspective, findings identify individual, programme, organization and entrepreneurship education ecosystem challenges for gender inclusive EET. Outcomes and impacts of interventions to inform EET that respond to the learning needs of diverse entrepreneurs, students and other stakeholders are discussed. The implications for practice and research are considered, and a summative model of gender inclusive EET is advanced.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it