Quantifying Common Trend of Gender Agricultural Productivity Gap in Sub-Saharan Africa: A Systematic Review and Critical Appraisals
Bibliographic record
Abstract
This study aims to provide a better understanding of the recent vast literature on gender agricultural productivity issues in sub-Saharan Africa (SSA). Due to some discrepancies in research findings, a systematic review and a meta-analysis methods are applied to synthesize the gender agricultural productivity gap (GAPG), and its main drivers as well as to explore the bias when using different empirical approaches of estimation. Overall, 61 studies are selected using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flowchart. Results show that factors related to access and control over productive resources are the main drivers of the GAPG in SSA. Adjusting for publication bias yields a benchmark estimate of 14% of the gap, based primarily on land productivity or output value. The magnitude of this common trend varies by geographical region but not by household framework used. Policy interventions aimed at reducing gender gap in SSA should then be context specific. Furthermore, the meta-regression analysis results reveal that GAPG estimates are affected by study characteristics like gender of the researchers and use of cross-sectional or panel data.
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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 | 0.057 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.023 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".