Ranking the cities with potential of tourism investment in Fars province using numerical taxonomy
Bibliographic record
Abstract
Tourism industry is one of the most important parts of economic development. It can create job either directly or through stimulating other economic sectors. Therefore, it is important for policy makers to determine the best places with good potentials for tourism development. Fars province has been the center of civilization and the origin of the Iranian literature and mysticism celebrities and could be a potential investment of tourism. However, this province does not have the infrastructure required to accommodate tourists, especially during holiday seasons. This study ranks different cities located in the Fras province having the necessary potential for investment in the tourism sector by using the numerical taxonomy in terms of 12 indicators. The research population includes 29 cities in this province based on the devision of 2016. The necessary data are collected from Directorate General of Cultural Heritage, Handicrafts, and Tourism in Fars province. It is found that the cities of Shiraz, Marvdasht, Firouzabad, Sepidan, and Kazerun were the best cities for tourism investment, respectively.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".