Building gender-aware ecosystems for learning, leadership, and growth
Bibliographic record
Abstract
Purpose The purpose of the paper is to examine processes of entrepreneurial learning and leadership development (ELLD) for women involved in growth-oriented businesses. It considers how ELLD can be supported by building gender-aware ecosystems for growth. Design/methodology/approach Data are from a small-scale study of a growth accelerator program in Canada run by Alberta Women Entrepreneurs. The study uses a mixed-methods approach, drawing on interview, document, and observational data. Findings The study finds that three key activities – formal learning, informal learning and peer / community support – are central to women entrepreneurs’ learning and leadership development. In line with emerging scholarship, entrepreneurial learning is found to be strongly relational, with social capital playing a central role in the formation of human capital. Originality/value This study contributes to the understanding of the micro-foundations of growth, the processes involved in ELLD and the importance of developing gender-aware ecosystems.
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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 | 0.001 | 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".