Who, Me? Increasing High School Girls’ Entrepreneurial Self-Efficacy, Knowledge and Intentions
Bibliographic record
Abstract
This paper provides a case study of two female-onlyentrepreneurship education programs designed by faculty from BresciaUniversity College, Canada’s only women’s university, located in London,Ontario, Canada. The programs were designed to address the substantialgender gap found in women’s participation in entrepreneurial activities byinspiring, educating, and exposing program participants to entrepreneurialendeavours. One program was a one-day conference and the other wasa one-week boot camp. The study was designed to better understand howto strengthen the female entrepreneurial pipeline by measuring changesin entrepreneurial knowledge, entrepreneurial self-efficacy (ESE), andentrepreneurial intentions (EI). Program participants were asked to completepre- and post-experience questionnaires where information aboutleadership experiences, role models, entrepreneurial knowledge, ESE, andEI was collected. The results of the analysis reveal that the gender-specificprogramming increased ESE in the one-week camp and that both programssignificantly increased both objective and self-perceived knowledge ofentrepreneurship. The authors conclude that the female-only educationalinterventions helped to transform adolescent girls’ sense of entrepreneurialpossibilities. We recommend a scaffolded and integrated approach to futureentrepreneurship education programming to address and ultimately closethe entrepreneurship gender gap.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".