Working Paper 357 - Impact of COVID-19 on Mining Case Studies of four African Countries
Bibliographic record
Abstract
The COVID-19 pandemic has affected many economies worldwide. It had diverse impacts on the African mining sector. This paper documents its impact by focusing on four major mineral-rich African countries: Ghana, Mali, South Africa and Zambia. An assessment of the impact of the pandemic on production, employment and government revenues in the selected countries is analysed in the paper. The effects of the pandemic created both supply and demand shocks, resulting in an overall decrease in most mineral prices (with the exception of precious metals such as gold). The situation persisted for much of 2020, with most prices mostly recovering only in 2021. It is also instructive to note that the pandemic is still on-going, thus the analysis is limited to the first quarter of 2021. In addition to reduced direct, indirect and induced employment in the mining sector, the pandemic shocks resulted in a decline in overall mining output across the four countries. This reduction was despite the positive effect on the price of gold. Mining revenues for the four countries also fell due to falling commodity prices and mining output. The paper provides some policy recommendations to ensure that the sector becomes resilient to possible future shocks of this nature.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".