Micro-, Small- and Medium-Sized Enterprises with High-Growth Potential in the Southern Mediterranean: Identifying Obstacles and Policy Responses
Bibliographic record
Abstract
The Arab Spring, which took root in Tunisia and Egypt in the beginning of 2011 and gradually spread to other countries in the southern Mediterranean, highlighted the importance of private-sector development, job creation, improved governance and a fairer distribution of economic opportunities. The developments led to domestic and international calls for the region’s governments to implement the needed reforms to enhance business and investment conditions, modernise their economies and support the development of enterprises. Central to these demands are calls to enhance the growth prospects of micro-, small- and medium-sized enterprises (MSMEs), which represent an overwhelming majority of the region’s economic activity. On the basis of interviews conducted among high-growth potential MSMEs in selected countries in the southern Mediterranean – Algeria, Egypt, Morocco and Tunisia – this report identifies and ranks key obstacles preventing MSMEs from reaching their high-growth potential and puts forward effective policy responses to reduce these obstacles. If implemented, the authors argue that these policies could unlock the MSMEs potential to contribute more to their economies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".