Québec SME Risk Management and Exports to Asian Countries
Bibliographic record
Abstract
Abstract Over the last 10 years, small- and medium-sized enterprises (SMEs) in developed countries have faced increasingly stiff competition in their local markets, which has put the survival of many of them at risk. To reduce their vulnerability, many SMEs have targeted sales to other countries. Recently, however, the pace and intensity of these firms’ export activities appear to have decreased, as their traditional markets (i.e., the United States and Europe) have been experiencing slow growth. This situation has led some SMEs to explore the possibility of exporting to less traditional countries presenting more opportunities. However, a good number of entrepreneurs remain hesitant to go down this road, in particular given the uncertainty that prevails in those regions and the risks they represent in terms of exports. This study, which was conducted with a sample of 582 Canadian manufacturing SMEs, reveals that two characteristics help explain the fact that some SMEs choose to export to higher risk countries, more specifically to Asia. These characteristics are a positive attitude towards risk-taking among managers and the implementation of certain risk management mechanisms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".