Methodological Evaluation of Quasi-Experimental Design in Smallholder Farms Systems in Ghana: A Systematic Literature Review
Bibliographic record
Abstract
Quasi-experimental designs are used to assess the impact of interventions in smallholder farms systems, particularly when randomization is not feasible due to resource constraints or external factors. A comprehensive search strategy was employed across multiple databases including Web of Science and Scopus. Studies were screened based on predefined inclusion criteria, with qualitative synthesis methods applied. The analysis identified a trend towards integrating econometric models to estimate treatment effects, with some studies reporting significant reductions in risk factors (e.g., $Y = eta_0 + eta_1X + u$, where Y is the outcome measure and X represents intervention variables; confidence interval of 95%). The review concludes that methodological rigor is essential for reliable quasi-experimental studies, with econometric models being a key component. Researchers are encouraged to adopt standardised econometric frameworks and transparent reporting practices to enhance the credibility and replicability of their findings.
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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 | 0.408 | 0.555 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".