Microfinance and post-conflict reconstruction : organizational capacity-building of a CARE International microcredit project in Jaffna, Sri Lanka
Bibliographic record
Abstract
The purpose of this thesis is to examine the challenges of capacity building of Community Based Organizations (CBO) in the microfinance sector, focused on poverty alleviation in Jaffna, Sri Lanka. This has been achieved by examining a case study of the pilot project, capacity building of CBOs in Jaffna (CAB-J project), being carried out by CARE International, Jaffna. For this purpose CARE has selected twenty CBOs to build their capacity in financial services to provide savings and credit services to vulnerable group of people. The ethnic conflict in Jaffna has caused massive destruction to the society, economy, public and private properties. Various subsidy and grants programs has developed a dependency syndrome among poor and they still remains as poor. They don't have adequate capital to invest in self employment activities. So there is an urgent need to build the local capacity in microfinance. The impact of the CAB-J microcredit program is analysed and discussed. Various challenges and implication of capacity building in microfinance in Jaffna post conflict context are also examined. A multiple method was used to collect necessary data and information including interviews, participatory observations, secondary data and comprehensive literature review regarding Jaffna context. The interviews primarily focused on the socio-economic impact of the project, various challenges and implications related to capacity building at institutional and grassroot level. The analysis was primarily based on the qualitative data. The study revealed that the CAB-J microcredit is an ideal tool in alleviating poverty in the post conflict context like Jaffna by strengthening the socio-economic structures of the Jaffna community rather than merely depend on grants or subsidy. But the actual impacts should be assessed in regards to the long term effect and sustainability of the program. But the project faced several challenges primarily lack of fund and human resources, high demand for non financial services, prevailing uncertain political situation, in building local capacity in microfinance. The study suggests that to achieve maximum outreach and sustainability some special components such as incorporating non financial services, blending the microcredit program with relief assistance, increasing human resources, and pouring more fund into the program should be incorporated into the existing programs
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".