Anecdotal Evidence of the Role of Incubation in the Growth of Business Start-Ups in Uganda
Bibliographic record
Abstract
Although incubation is a well-known and accepted strategy for the growth of business start-ups in Uganda, little is known about its extent. What is known is that the majority of the start-ups fail in less than one year. This study explored the role of incubators on the growth of business start-ups in Uganda. The study interviewed managers of the incubation services and business starts-ups on how they received this support. The study is qualitative and respondents were purposively selected. Key informant interviews and focus group discussions were used to capture the perceptions of the services and how the support enabled the enterprises to grow. The findings show that various services were offered to support the growth of business start-ups. The services ranged from the creation of networks to other business development services such as mentorship, coaching, and marketing. The perceptions of the owners of business start-ups were somewhat mixed because while the majority viewed the support as crucial in the growth of their start-ups, a few others said they did not. Thus, a few owners of business start-up viewed incubators as playing a limited role. The findings are pertinent for policy formulation on the role of business start-ups and for streamlining incubation support processes in emerging economies.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".