Islamic Development Bank’s Microfinance Support Programme and the Growth of Small Scale Enterprises in Nigeria
Bibliographic record
Abstract
It is obvious that poverty is endemic in Nigeria. One of the reasons contributing to this uncomplimentary situation is lack of employment for the majority of Nigerians. Several attempts aimed at creating employment have been embarked upon at governmental and non-governmental levels, with little effect. On the other side, the Islamic Development Bank (IDB), to which Nigeria is a key member, has been actively involved in the support of Small Scale Enterprises in some member countries through skilfully carved sustainable non-interest-based microfinance programme. Most studies on microfinance in Nigeria have not paid attention to the application of the IDB’s Microfinance Support Programme to Nigeria. Hence, this paper fills the gap through examining some of the past and present attempts of the government at providing microcredit and microfinance to Nigerians. It also considers the operational strategy utilised by the IDB at bringing sustainable microfinance scheme to the doorsteps of the poor in member countries. Consequently, it advocates this laudable microfinance scheme for Nigeria, arguing that if this is done, mass employment accompanied by best practices capable of reducing poverty would be injected into Nigeria.
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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 | 0.000 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".