COVID-19 in Zambia: Implications for Family, Social, Economic, and Psychological Well-Being
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted Zambian life. Prior to the pandemic, persistent levels of inequality across urban/rural, educational, and socioeconomic divides characterized Zambia’s health and family patterns. Zambia’s government reacted decisively to the threat, shutting down many businesses, schools, and other social gatherings prior to the first confirmed cases. Fortunately, the country has not had many deaths. The indirect effects of COVID-19, however, on Zambian families mean many have lost income (via a reduction in the size of both the formal and, particularly, the informal sectors) and many businesses struggle with increased costs and reduced revenues. The healthcare system, particularly in rural areas, is strained. While the effect on the education system likely will not be fully visible for many years, it is likely that fewer children will pass their examinations this year, thereby reducing human capital in the next generation. In contrast, Zambia’s wealthy, often concentrated in the two economic and population hubs of Lusaka, the capital, and the Copperbelt province, are spending greater time with their families while using their substantial resources to maintain their standard of living. The ability to engage in physical distancing varies as well, with the privileged able to do so by staying home and shopping at smaller, more expensive (but less patronized) shops while the poor crowd into congested neighborhoods and markets. These deep inequalities will likely continue to shape Zambian society in both the near and long-term future, well after the COVID-19 pandemic of 2020 has faded from collective memory.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".