Barriers to informal social protection in Uganda: insights from beneficiaries of Village Savings and Loan Associations
Bibliographic record
Abstract
Purpose While the success of Village Savings and Loan Associations (VSLAs) has been documented, little is known about the factors affecting their performance, particularly in Uganda. This study investigated the barriers to informal social protection interventions witnessed by beneficiaries of VSLAs in Kampala District and Alebtong District, Uganda. Design/methodology/approach This study employs a quantitative approach using a questionnaire survey to collect data from a sample of 130 beneficiaries. Descriptive statistics were used to analyse the data. Findings The empirical results identified several barriers to informal social protection interventions with reference to VSLAs, with financial, structural and implementation challenges emerging as the most prominent. Statistical analysis confirmed that these challenges are significant, underscoring their critical impact on the effectiveness of the VSLAs. Practical implications These results are essential for policymakers, development practitioners and community leaders seeking to enhance the effectiveness in fostering economic resilience, social cohesion and community empowerment. Originality/value This study’s insights into the differing dynamics between on-farm and non-farm VSLAs provide a foundation for designing context-specific interventions that address the unique challenges faced by each group. Further, by informing targeted strategies to overcome these barriers, the findings contribute to strengthening informal social protection systems in Uganda and other similar contexts. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2024-0639.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".