Syrian SMEs in Times of COVID-19 Pandemic: Challenges, Adaptation, and Policy Measures
Bibliographic record
Abstract
SMEs constitute the backbone of the Syrian economy and have suffered manifold challenges due to the continuous Syrian war. COVID-19 added further pressures on Syrian SMEs and forced them to take certain adaptation strategies to survive. This paper aims to investigate the main challenges that face Syrian SMEs during the pandemic and illustrate how they respond to adversities that emerged from governmental intervention to control the spread of the virus. It also discusses the measures initiated by the government to support SMEs during the pandemic. Through interviewing persons from the Syrian SMEs’ ecosystem, we find that high interest rates on SMEs’ loans decline on demand as well as high inflation represent the main challenges. SMEs respond to these challenges by marketing products online, stock procurement, and strengthening connections with stakeholders. We recommend the Syrian authorities reduce lending rates and increase loan sizes available to SMEs to help them overcome the pandemic adversities. Innovative sources of funding, such as venture capital and equity partnerships, could reduce the funding costs of SMEs. Moreover, SMEs will immensely benefit from training in digital tools to enhance their expansion and survival opportunities. Furthermore, bazaars should be organized during the year to give SMEs the opportunity to gain continuous access to markets. In addition, incubation services should be revised, particularly to SMEs with great potential to grow, to create the suitable environment for them to scale and flourish.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".