Bibliographic record
Abstract
Objective – This paper offers a review of the latest studies with regards to the impact of COVID-19 on small businesses in different countries around the world. Methodology – This paper reviewed a compilation of COVID-19 studies focusing on SMEs that was conducted between 2020 and 2022. The review enables us to understand the globally common or underlying challenges to SMEs due to COVID-19, along with an assessment of government’s initiatives that were implemented to alleviate the impact. The review revealed that the pandemic caused a major disruption for small businesses which also acts as a catalyst towards digitization and innovation towards competitiveness which is facilitated by government initiatives. The review process comprises systematic and vast-ranging search for articles related to the subjects to look for evidence, and secondly, for limit the risk of biasness. Findings – This survey of experiences elsewhere might provide insights to policymakers in countries that are struggling to cope with the problem on the initiatives to consider and the additional initiatives that might be necessary to make them effective in their individual country contexts. Novelty – Given limitations of space, we survey only a limited sample of countries from Asia and Europe, along with the US and Canada. Hopefully, their experiences will provide a broad enough spectrum of initiatives for policymakers elsewhere to consider and evaluate. Type of Paper: Review JEL Classification: M21, O38
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".