Small and Medium Enterprises and Global Risks: Evidence from Manufacturing SMEs in Turkey
Bibliographic record
Abstract
Abstract This study investigated how small and medium enterprises (SMEs) in a country perceive major global risks. The aim was to explore how country attributes and circumstances affect SME assessments of the likelihood, impacts, and rankings of global risks, and to find out if SME risk assessment and rankings differ from the global rankings. Data were gathered using an online survey of manufacturing SMEs in Turkey. The results show that global economic risks and geopolitical risks are of major concern for SMEs, and environmental risks are at the bottom of their ranking. Among the economic risks, fiscal crises in key economies and high structural unemployment or underemployment were found to be the highest risks for the SMEs. Failure of regional or global governance, failure of national governance, and interstate conflict with regional consequences were found to be among the top geopolitical risks for the SMEs. The SMEs considered the risk of large-scale cyber-attacks and massive incident of data fraud/theft to be relatively higher than other global technological risks. Profound social instability and failure of urban planning were among the top societal risks for the SMEs. Although the global environmental and disaster risks were ranked lowest on the list, man-made environmental damage and disasters and major natural hazard-induced disasters were ranked the highest among this group of risks. Overall, the results show that SMEs at a country level, for example Turkey, perceive global risks differently than the major global players.
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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 | 0.001 | 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".