Implications of COVID-19 Lockdown on South African Business Sector
Bibliographic record
Abstract
The COVID-19 pandemic in South Africa spelt untold adversity on many businesses in the country. This is attributable to the precautionary measures implemented by the South African government to contain the ferocious disease spread across various levels in the society. This article examined the many implications of the pandemic on the business sector in South Africa through the period just before June 2020. The article adopted a qualitative approach to critically examine the implications of the COVID-19 pandemic on the business sector in South Africa. It utilised Statistics South Africa’s reports and contemporary literature on the effects of COVID-19 on the business sector which was used as a source of reference. The statistical evidence is based on experimental data for May 2020, as derived from over a thousand registered businesses operating within the formal sector from various industrial groupings in South Africa. The period under review is remarkable because the business's effect of the pandemic became visibly alarming while the lockdown phases were reduced from Level 5 to Level 4 within the period. The private sector of the economy, known for its huge economic sustenance of most of the country’s labour force, was already at the firing line as all ‘nonessential’ businesses succumb to government’s strict regulations on socioeconomic activities during the phased lockdowns. The hitherto struggling economy, barely sustained by this sector, eventually caved in with over half of its gross domestic product, shrinking attributable to the unparalleled adverse effects that attended the dreaded pandemic. Although government is still battling with the nation’s economic recovery process, many businesses counted their losses from the first few months of the lockdown, as portrayed by plentiful evidence from the national statistical body.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".