Gender Disparity Among Cooperative Farmers in Accessing Agricultural Credits in Anambra State, Nigeria
Bibliographic record
Abstract
Women have been the focus of gender disparity and this has been widely referred to the disparity faced by women in the field of agriculture. Agricultural credit is imperative for sustainable agricultural development in any country of the world. In order to substantiate the assertion, this study evaluated the issues of gender disparity in farmers’ access to agricultural credit among cooperative societies in Anambra north zone of Anambra State. Specific objectives were to ascertain the quantum of credit obtained and repaid by female and male members; determine the effect of gender on the quantum of credit obtained and repaid; ascertain critical factors influencing access to credit by cooperative members; determine how gender contributed to credit repayment behaviour of cooperative members and examine perception of members on gender-related issues in credit operations. ANOVA and regression models were used to test hypotheses 1-5. Findings revealed that male members obtained more credit than female members, and female members repay more than their male counterparts. Gender was not a significant determinant of credit obtained and repaid by cooperative members and gender issues in credit operation were handled among cooperative members. However, the researcher recommended that the issues of gender inequality should not be encouraged. Both males and females should have equal access to credit and repayment of credit operation; despite the membership strength, more members should be encouraged to join cooperative societies in order to access credit and repay accordingly and cooperative officers should set up friendly credit scheme to ensure a functional and effective credit access.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".