Financial Inclusion in Rural and Urban Nigeria: A Quantitative and Qualitative Approach
Bibliographic record
Abstract
The study provided insights into current realities of financial inclusion among financially vulnerable (financially illiterate and semi-literate) customers in an emerging economy. The two-phased study adopted both quantitative and qualitative methods in which cross-sectional and phenomenological approaches were used for data collection, with specific emphasis on rural-urban differentials. Data for the first phase was obtained from an urban (n=211) and rural (n=242) sample selected via a combination of purposive and convenient sampling. A structured questionnaire was utilized in eliciting relevant information from the study participants. Data for the second phase was obtained from bank managers who are key informants with professional knowledge about trends of financial inclusion in Nigeria. Quantitative outcomes showed that residential status had a significant main effect on access to marketing financial inclusion services, such that rural residents had limited access to financial inclusion services; while perceived cost of financial inclusion had a significant main effect on usage of financial inclusion services, such that perceptions of high cost of perceived inclusion resulted in less usage of financial inclusion services. Qualitative outcomes highlighted major efforts used to drive financial inclusion including financial education and financial literacy in rural Nigeria, while highlighting the prospects, problems and possible interventions.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".