Can Entrepreneurship Education Reduce Stereotypes Against Women Entrepreneurship?
Bibliographic record
Abstract
The aim of this study is to investigate whether entrepreneurship trainings can reduce stereotypes against women entrepreneurship. With this aim socio-psychological obstacles to women entrepreneurship in Turkey are examined, and an experimental study is carried out. Entrepreneurship courses were given with a special emphasis on women entrepreneurship and gender issues. To evaluate the out puts of the experimental study, a questionnaire was designed and applied to students. In order to make a comparison, the same questionnaire was applied to students from two other universities, who have taken entrepreneurship courses through the classical method and curricula. Survey tool includes 21 statements on women entrepreneurship, 5 positive and 16 negative. A non-parametric Mann-Whitney U test was conducted to evaluate the hypothesis that the experimental group would score lower in negative statements and higher in positive statements, on the average, than the non-experimental group. Test results indicate that mean ranks for the two groups differ significantly from each other in 12 items (p<.05). Experimental group score significantly higher than the non-experimental group in 4 positive and 1 negative statements; and lower than the non-experimental group in 7 negative statements. Compared to non-experimental group, experimental group seems to have higher scoring for awareness and advocacy of women entrepreneurship and lower scoring for socio-psychological obstacles against women entrepreneurship. According to the survey results, it can be concluded that modification of entrepreneurship education curricula will contribute to reduce stereotypes hindering women entrepreneurship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".