Gender Effect on Adoption of Selected Improved Rice Technologies in Ghana
Bibliographic record
Abstract
The study sought to test the hypothesis that gender influences adoption of innovations in the rice sector of Ghana. There is an existence of gender gap in adoption of farm innovations in Ghana. After desk review, it was found that the existing literature has not provided a clear linkage between gender and adoption of agricultural technologies. Thus, the objective of this study was to determine how the interaction between gender and other socio-economic factors influence the incidence of adoption of improved rice variety and fertilizer. Drawing on 917 face-to-face interviews with rice producers, the results show that child care and limited access to land inhibit female incidence of adoption. It is recommended that the innovation system should take cognizance of female reproductive role and develop, as much as possible, technology options that rely less on intensive use of labour. Furthermore, government should facilitate the development of land markets to improve female access to land, especially in northern Ghana where cultural norms restrict women’s access to land.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".