IMPACT OF CORONAVIRUS ON SMALL AND MEDIUM ENTERPRISES (SMES): TOWARDS POST-COVID-19 ECONOMIC RECOVERY IN NIGERIA
Bibliographic record
Abstract
In the recent time, coronavirus (COVID-19) becomes an emerging area of research especially in exploring its effects from multidimensional perspectives such as health, educational and economic perspectives. More importantly, there is an insufficient academic research in connecting the impact of COVID-19 with Small and Medium Enterprises (SMEs) in Nigeria despite the fact different countries of the world such as US, UK, Canada, and China etc. have been making tremendous effort in addressing the economic impact of COVID-19. The primary objective of this paper is to explicitly make a shift by building a comprehensive theoretical basis for the impact of COVID-19 on SMEs in order to chat a forward for post-COVID-19 economy recovery to thrive in the country. This study used secondary data to gather vital information by exploring available materials or literatures in this regard. The findings of this paper indicated that, recent study provides health implication of COVID-19 which has overwhelmingly explained by World Health Organization (WHO). Hence, this paper argues that appropriate measures should be provided especially by giving loan support to SMEs in expanding and strengthening the existing and new business opportunities as response to the impact of post-COVID-19 economy recovery in the country. It is therefore suggested that collaboration between SMEs leaders and the government have vital roles to play especially through the activities of Small and Medium Enterprises Development Agency of Nigeria (SMEDAN) in providing platform for inclusion of digitization into SMEs or business operation in the country.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".