Assessing Rural Banks Effectiveness in Ghana
Bibliographic record
Abstract
This study critically examines contemporary issues and lending activities to small scale farmers for agricultural production. The Agriculture sector is the mainstay of Ghana’s economy and small scale farmers play a dominant role in the sector which explains why this study concentrates on this sector. A survey research was conducted using both structured and unstructured questionnaires. A total of 127 farmers, 18 key informants and 10 rural banks were interviewed. Descriptive and inferential statistics were used to analyse the effectiveness of rural banks. Allocations of the various loans (agriculture, cottage industry, trade and transport and others ¾ social credit) in the rural banks’ credit portfolio were significantly different among the four loans categories (ANOVA p = 9.6E-29). From the tukey-kramer procedure, there was a difference in average amount of loan disbursed between agriculture and trade, and between agriculture and social credit with Q-Statistics of 3.84. The means for trade and social credit were larger than that of agriculture and by implication agriculture is treated less favourably in rural banks credit schemes and portfolio. These findings lend credence to the claim that rural banks are not sticking to their core mandate of prioritising credit provision to rural agriculture and have strayed into other endeavours.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".