Characterization of Enterprises Financed by Public and Private Credit Systems in Tanzania
Bibliographic record
Abstract
Abstract: A study was conducted in purposefully selected 10 districts in two regions, involving 166 enterprises funded by the government scheme and 201 privately financed initiatives, using participatory tools to identify setbacks. Respondents comprised operators (263) and policy makers (30) for the government and 201 private initiatives. All credit schemes targeted lowly educated groups, comprising women (61.6%), youths (34.6%) and disabled people (3.8%). Approximately 51% of the enterprises focused in agriculture whereas, others mainly centered on petty businesses (36.8%) and motorbike riding (5.4%). However, all initiatives lacked affirmative strategies for disabled people and business linkages whereas, constitutions were also not operational post-government funding. Skills in entrepreneurship, soft skills, packaging, labelling and price setting were not provided in all schemes. Furthermore, traceability, due diligence and supervision mechanisms were poor and fund divergence was common in the government scheme. Default rates in government-financed enterprises were high but low (5%) in private credit schemes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".