Budgetary Allocations and Government Response to COVID-19 Pandemic in South Africa and Nigeria
Bibliographic record
Abstract
The eruption of the novel virus brought to the global scene the prediction that Africa would be worse hit by the pandemic. This prediction was partly built on the widely recognized fact that Africa is the continent with the weakest public health care system and the lowest budgetary allocations to health. However, contrary to this prediction, the COVID-19 death rate in Africa has been low compared to in other continents. Debates on Africa’s low COVID-19 death rate have generated mixed reactions, the majority of which have centred on beliefs and superstition about hot weather and Africa’s youth-dominated society. Little or none of these reactions have attributed the low COVID-19 death rate to swift and prudent budgetary adjustment, which partly aided a swift response from some African governments. Indeed, not many studies have examined the swiftness in the response of some African governments and prudent budgetary adjustment in tackling the spread of COVID-19. This paper, through secondary data, advances knowledge on how budget revision aided government response to the COVID-19 pandemic in South Africa and Nigeria. It found that both countries adjusted their budgetary allocations in response to COVID-19. It further indicates that South Africa, through budgetary revision, allocated more funds to government agencies in charge of COVID-19 and various relief packages than Nigeria. Moreover, it indicates that the swift budgetary adjustment by both countries partly aided a quick government response that progressively flattened the curve and, in the long run, partly contributed to fiscal impulse and deferrals.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".