Globalisation in the time of COVID-19: repositioning Africa to meet the immediate and remote challenges
Bibliographic record
Abstract
The COVID-19 pandemic has ushered in a new climate of uncertainty which is fuelling protectionism and playing into nationalist narratives. Globalisation is under significant threat as governments scramble to reduce their vulnerability to the virus by limiting global trade and flows of people. With the imposition of border closures and strict migration measures, there have been major disruptions in Africa's global supply chains with adverse impacts on employment and poverty. The African economies overly reliant on single export-orientated industries, such as oil and gas, are expected to be severely hit. This situation is further aggravated by tumbling oil prices and a lowered global demand for African non-oil products. The agricultural sector, which should buffer these shocks, is also being affected by the enforcement of lockdowns which threaten people's livelihoods and food security. Lockdowns may not be the answer in Africa and the issue of public health pandemic response will need to be addressed by enacting context-specific policies which should be implemented in a humane way. In addressing the socioeconomic impact of COVID-19 on African nations, we argue that governments should prioritize social protection programmes to provide people with resources to maintain economic productivity while limiting job losses. International funders are committing assistance to Africa for this purpose, but generally as loans (adding to debt burdens) rather than as grants. G20 agreement so suspend debt payments for a year will help, but is insufficient to fiscal need. Maintaining cross-border trade and cooperation to continue generating public revenues is desirable. New strategies for diversifying African economies and limiting their dependence on external funding by promoting trade with a more regionalised (continental) focus as promoted by the African Continental Free Trade Agreement, while not without limitations, should be explored. While it is premature to judge the final economic and death toll of COVID-19, African leaders' response to the pandemic, and the support they receive from wealthier nations, will determine its eventual outcomes.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".