Do COVID-19 and Food Insecurity Influence Existing Inequalities between Women and Men in Africa?
Bibliographic record
Abstract
This review sought to understand what is currently known about how the ongoing COVID-19 pandemic and restrictive measures are affecting food security and equality between women and men in all of Africa. A review of both the academic and grey literature was performed by following PRISMA guidelines. Results showed that a general disparity exists in gender-inclusive/-sensitive research. Most reported increases in inequalities between women and men were predictive only. Evidence-based articles found were mainly conducted online and target tertiary educated populations, among which neutral effects were found. A general lack of disaggregated data (e.g., women vs. men) was found to be a barrier in gaining a complete understanding of the situation on-the-ground. Furthermore, documents reporting on food security seldom included all four pillars (i.e., availability, access, utility, stability) in their analysis despite the reciprocal connection between them all. Within household disparities and the impacts on power relationships within households were also overlooked. Future studies must focus on rural settings and gender disaggregated interview processes as well as consider all pillars of food security. Doing so will help to better inform governments and humanitarian groups leading to better designed policies and social supports that target where they are most needed.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".