COVID-19 Pandemic and Its Implications on Small and Medium Enterprises (SMEs) Operations in Zambia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.009 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it