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Record W1838067612 · doi:10.3968/4260

Islamic Development Bank’s Microfinance Support Programme and the Growth of Small Scale Enterprises in Nigeria

2014· article· en· W1838067612 on OpenAlexvenueno aff
Mubarak Ademola Noibi

Bibliographic record

VenueCanadian social science · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsMicrofinanceNigeriansPovertyIslamBusinessScale (ratio)Government (linguistics)Economic growthSustainable developmentDeveloping countryDevelopment economicsEconomicsPolitical scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.193
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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