Gender Differences in Entrepreneurial Attitudes and Constraints: Do the Constraints Predict University Agriculture Graduates’ Attitudes towards Entrepreneurship?
Bibliographic record
Abstract
This quantitative study analyzed and predicted gender differences of agriculture graduates’ attitudes towards and challenges in entrepreneurship in Botswana. The study adopted a descriptive and correlational survey research design. A valid and reliable questionnaire was used for data collection through a survey of randomly sampled 149 final year agriculture graduate students (n=149). Inferential statistical tools of Independent t-test and Regression analysis were used for data analysis. The findings of the study determined three important attitudinal factors as: entrepreneurship results in economic growth of a country, employability and income generation and, entrepreneurship improves individual and social growth. Three important constraints in entrepreneurship were lack of land, lack of proper infrastructure and, lack of capital. These top attitudinal factors as well as constraints were the same for the male and female graduates despite of their ranking and importance. A gender difference in students’ attitudes towards entrepreneurship was established while no gender difference in the challenges in entrepreneurship was found. Out of fifteen constraints in entrepreneurship under study, only three constraints namely, lack of land, high competition in market and lack of capital, were determined as significant predictors of the graduates’ attitudes towards entrepreneurship. It is recommended that these three factors be made priorities while making policies for entrepreneurship development in the country. Further study is recommended to explore the perceptions of graduates on the possible ways to improve on these three predicting constraints and explore latent constraints predicting graduates’ attitude towards entrepreneurship. Those findings may provide better ideas in planning policies for entrepreneurial development among agriculture graduates in Botswana.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".