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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".