Socio-Economic and Political Impact of Pandemics in the African Continent and Regional Mechanisms to Mitigate it
Bibliographic record
Abstract
Throughout the history of African societies, pandemics have claimed, in some instances more lives than warfare. Africa is susceptible to many pandemics. Given the state of underdevelopment amongst African nation-states characterised by low levels of education, poor health care facilities, lack of basic infrastructure, poverty, low levels of income, lack of skilled health care workers, and many more factors, it is not sufficiently equipped to handle pandemics that are life-threatening. Hence, it is prone to outbreaks of infectious diseases. Pandemics cause the socio-economic crisis, which in turn affects political stability. In the history of Africa, the Ebola disease, HIV/Aids, Cholera are some of the major diseases that have ravished nation-states in contemporary times. Now, just like other parts of the world, it has to deal with the Covid-19 pandemic that has far-reaching consequences. This article seeks to interrogate the nature and causes of major pandemics in the globe and the African continent and the steps taken to ameliorate these. It further examines the impact of pandemics on the socio-economic and political spheres of life in the continent.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".