Global franchising in emerging and transitioning economies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Franchising has experienced phenomenal growth both in the US and abroad in recent years. Figures vary, but it is estimated that U.S. franchising generates $800 billion worth of business in gross sales and represents 40 percent of the retail trade (Swartz, 2001). While in the US, Canada and parts of Western Europe franchising has reached domestic market saturation, emerging markets remain relatively untapped. Emerging markets, accounting for 80% of the world’s population and 60% of the world’s natural resources, present the most dynamic potential for long-term growth to businesses, in general, and to franchisors, in specific. The U.S. Department of Commerce estimated that over 75% of the expected growth in world trade over the next two decades will come from emerging countries, particularly Big Emerging Countries, which account for over half the world’s population but only 25% of its GDP. Emerging markets are among the fastest growing markets for international franchisors. Several surveys conducted by Arthur Andersen showed that more and more franchisors are seeking opportunities in emerging markets. A recent article in Franchising World (Amies, 1999) stated: “Franchises are springing up in the most unlikely, and for many of us unheard-of, places...Those franchisors who can establish a beach-head on these wilder shores could do very well, but the risks are great.” This article is a step in the direction of educating its target markets about international franchising opportunities and threats in emerging economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it