COVID-19 PANDEMIC AND ECONOMIC CRISIS IN NIGERIA: THE EXPERIENCE OF SELECTED MICRO, SMALL AND MEDIUM-SIZED ENTERPRISES IN LAGOS AND OSUN STATES
Bibliographic record
Abstract
The world was thrown into the worst economic crisis since World War II in the first quarter of 2020 following the outbreak of covid-19 in Wuhan China. The pandemic forced many economies in the world to lockdown making economic activities to come to a standstill. Nigeria Micro, Small and Medium-sized Enterprises (MSMEs) have been the worst hit by this crisis due to the fragile nature of Nigeria economy before the lockdown. Considering the enormous contributions of this sector to the Nigerian economy, it is obvious that anything that affects this sector would automatically affect the entire microeconomic landscape in the country. This study therefore investigated how covid-19 lockdown affected MSMEs in Nigeria. The study used purposive and simple random sampling to choose twenty industries and sixty participants (managerial level) within Lagos and Osun States to participate in this study. Using descriptive analysis, it was discovered that the lockdown which lasted for more than five months ( March- August,) 2020 has affected many of the MSMEs negatively while few were able to take advantage of the lockdown to diversify their business activities. It was therefore recommended that, prolonging the lockdown would further worsen the situation of these MSMEs, leading to further loss of investments and jobs. Thus, the Federal Government’s N50 billion Targeted Credit Facility (TCF) which was a stimulus package for households and Micro, Small and Medium Enterprises (MSMEs) should be judiciously disbursed to save the economy from further sliding into crisis and/or possible recession.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".