EXPANDING SMALL BUSINESSES IN DEVELOPING COUNTRIES: ANALYZING RECENT TRENDS AND EXPLORING NEW GROWTH STRATEGIES IN THE GLOBAL SOUTH
Bibliographic record
Abstract
Small businesses are the microcosm of developing countries economy. They are the oil that keeps the citizens going. They come in form of neighbourhood kiosk seller, to airtime or food vendors, among others. Their key role informs this study to undertake a review of the expansion of small businesses in developing countries with in-depth analysis of the recent trends. This is with a view of exploring the new growth strategies in the global south. Small and Medium-sized Enterprises (SMEs) play a significant role in the global South, contributing substantially to employment, economic growth, and poverty reduction. Recent trends in SMEs in the global South highlight their resilience, adaptability, and increasing adoption of technology. It was observed that there is an estimated 89 million SMEs exist in the developing countries and contribute 70% of formal employment in developing countries. SMEs contribute up to 45% of GDP in developing countries. Women are also driving the growth of SMEs with 50% contribution in Africa. In the United States, women-owned businesses employ nearly 9 million people and generate over $1 trillion in revenue. In India, women-owned businesses create over 13 million jobs and contribute over $200 billion to the economy. The study also gave useful suggestions needed to expand small businesses in developing countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".