The Role of Tourists’ Offices in the Tourists’ Attraction and Their Contentment: Case Study of Isfahan, Iran
Bibliographic record
Abstract
The tourists’ offices are considered as one of the basic infrastructures for the development of tourism and the function of these offices implies the tourism condition and the related services in each society. These types of organizations try to provide the traveling information and activities and also what are required by the tourists. The main duty of the tourists’ offices is the fulfillment of the tourists’ demand or motivating people toward touristic attractions and predicting people’s incentives for traveling. The purpose of the present study is not only determining the effective factors on the function of such offices, but also evaluating these offices by the foreign tourists in order to state the strengths and weaknesses of the tourists’ offices of Isfahan and propose some strategies to improve the quality and quantity of the services presented by these offices. The methodology of the present study is analytic- descriptive, in this regard besides doing some library studies and field studies, the required information has also been collected. The results show that the mere existence of different tourists’ offices cannot be regarded as the main factor of attracting the tourists and especially the foreign tourists, but some other factors like proper facilities, organized planning, vast and disciplined advertisements and also the familiarity of the tourists’ offices clerks with the international languages can be effective. Consequently, the tourists’ contentment is achieved and the economic situation of the country will be developed through attracting so many tourists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".