Quantifying Common Trend of Gender Agricultural Productivity Gap in Sub-Saharan Africa: A Systematic Review and Critical Appraisals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it