The Propensity of Male vs. Female Students To Take Courses and Degree Concentrations in Entrepreneurship<sup>1</sup>
Bibliographic record
Abstract
As of 2004, only 33% of the self-employed in Canada were women, and Industry Canada (2002) reports that in 2000, only 15% of lead entrepreneurs were women. However, as of 2002, approximately equal numbers of men and women were enrolled in Faculties of Business across Canada. Bird and Brush (2002) suggest that education plays a major role in explaining the disparity in venturing rates between women and men. One of the two studies reported in this paper (Study A) investigated the number of women vs. men enrolled in entrepreneurship courses across Canada and found that in almost all instances men greatly outnumber women in undergraduate and, more particularly so, in graduate courses. Study B investigated at one university whether women choose to take a business concentration in entrepreneurship as frequently as male students and found that mostly male students concentrate in entrepreneurship. There was a significant difference between women and men in one reason for not taking an entrepreneurship concentration: women were more likely to say that entrepreneurship did not fit their personality. There was no difference between men and women regarding their attitude to risk-taking aspects of entrepreneurship, which contradicts some previous research. The two studies reported in this paper have implications for entrepreneurship education, and for the training of female management students.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".