Bibliographic record
Abstract
The article is devoted to analyzing the current state of the global franchise market and the study of problems, trends, and prospects for its development. The study’s primary purpose is to substantiate the impact of the franchise market on the development of the hospitality sector. The study results show that today the franchise market is developing rapidly, with the most active in the hospitality industry, formed by the hotel and restaurant business. Today the franchise market is most actively developing in fast food. Analysis of the development trend in recent years has shown a significant market decline in 2020 due to the pandemic. It proves that the franchise market is a driver of the development of the hospitality industry, as the growth rate of income of companies that work in franchises exceeds the total income of companies in the hospitality industry. At the same time, the impact occurs not only in the economic aspect but also in the social one. Franchising is a stimulant for employment growth. It also helps to improve the quality of services and stimulates the development of small businesses. The paper also summarizes the main discussion issues of the positive impact of franchising in the hospitality sector. The primary trend in market development is the digitalization of tourism technology.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".