The Influence of Geographical Coverage on the Microfinance Sustainability and Outreach in Northern Ghana
Bibliographic record
Abstract
The study examined for statistically significant relationship between geographical coverage of microfinance institutions and sustainability and outreach from the view point of managers and operational staff in northern Ghana. Structured questionnaire was used in collecting data. The questionnaire was administered to a sample of 181 managers and operational staff of 18 microfinance institutions. The study used primary data. In selecting the respondents for this research paper, purposive and convenient sampling techniques were employed. The questionnaire was personally administered by the researcher. The study was conducted to ethical standards and respondents were made aware that participating in the study was voluntary. Data collected was analyzed using Spearman’s correlation and descriptive statistics. The research uncovered a statistically significant positive relationship between geographical coverage and sustainability and outreach in northern Ghana using Spearman’s correlation. In addition, the use of descriptive statistics showed that geographical coverage of microfinance institution influenced its sustainability and outreach with particular reference to the number of clients served, location of offices or branches, and scope of coverage. This study adds to the literature on geographical coverage and microfinance sustainability and outreach in the context of northern Ghana. This study is limited to only northern Ghana and not Ghana in its entirety.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".