The Development of Inclusive Agriculture Entrepreneurship Education Ecosystems for Young Entrepreneurs in Uganda
Bibliographic record
Abstract
This study examines how Uganda’s agricultural entrepreneurship ecosystem influences the success and participation of young entrepreneurs. By exploring the perspectives of entrepreneurial youth, this research provides grassroots insights into the structural and social factors shaping their experiences. Using semi-structured interviews with 16 student entrepreneurs, we employ discourse analysis to identify key themes that highlight both opportunities and barriers within Uganda’s entrepreneurial landscape. Our findings reveal three dominant discourses: (1) youth are encouraged by their families and communities to pursue entrepreneurship as a means of securing their livelihood in response to limited formal employment opportunities; (2) access to capital and financial literacy gaps remain significant barriers, limiting students’ ability to scale their businesses; and (3) gender dynamics shape entrepreneurial participation, with women facing additional hurdles that restrict their business opportunities. These insights contribute to the broader literature on inclusive entrepreneurial ecosystems, emphasizing the need for targeted policy interventions that enhance financial access, promote gender equity, and support locally driven entrepreneurship initiatives. By addressing these challenges, Uganda’s entrepreneurial landscape can better foster sustainable economic opportunities for young entrepreneurs.
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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 | 0.003 | 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 | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".