COVID-19 Pandemic and Its Implications on Small and Medium Enterprises (SMEs) Operations in Zambia
Bibliographic record
Abstract
The COVID-19 pandemic has slowed down the operations of enterprises of different sizes and types in different ways. The most affected are the SMEs operating in various sectors of the economy. This study sort to investigate the influence of the COVID-19 pandemic on the operations of SMEs in the food and accommodation industry and provide policy recommendations to the government on supportive measures for SMEs. We employed an exploratory methodology with a critical review of available literature, including policy documents, research papers, and relevant literature to the sector Data was collected from four provinces using a survey method, and analysis was conducted through descriptive statistics. The findings indicate that most of the SME's monthly revenues have gone down by more than 50 percent and they are facing challenges such as failing to pay workers, restricted number of customers, and high cost of inputs. Besides, 21 percent of the SMEs reported improved adherence to health guidelines as one of the mitigating factors to minimise the spread of the COVID-19 pandemic. Furthermore, only 4 percent of the SMEs have accessed financial support from Government but their businesses have remained the same. Based on these findings, policy recommendations have been made to help SMEs survive during the crisis.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".