Gender Dimensions of COVID-19 and Social Policy in Sub-Saharan Africa
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic is highly gendered and social policy measures that were implemented to mitigate the impact of the disease on the lives and livelihood of the affected population in sub-Saharan Africa (SSA) reveal a gendered approach by governments in SSA. Since its outbreak in 2019, the COVID-19 pandemic has undergone several phases, with cases rising sporadically in some countries compared to others. At the global level, COVID-19 cases rose from over 79 million in 2020 to over 500 million as of 2022, and reported deaths rose from over 1 million in 2020 to more than 6 million as of May 2022 (OWD, 2022). The COVID-19 pandemic is not only a health concern, it affected all areas of human life globally. In particular, the pandemic outbreak has amplified the pre-existing vulnerabilities and structural inequalities that exist in SSA, where most of the world’s vulnerable to socio-economic shocks live.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".