The role of cultural and substructure components on decreasing sport tourism in Iran.
Bibliographic record
Abstract
The purpose of this study was that the role of Iran' cultural and infrastructure on reduce traveling sports tourism to review. The research method in this study was Descriptive and field. For this purpose, Mahmoodi Yekta and et al' questionnaire (2012) was used. Reliability of questionnaires after a pilot study using Cronbach's alpha test, (α=0.85) was obtained. Statistical population of survey is all an active sport tourists from Russia, Turkey, Armenia, Uzbekistan, Greece, Sweden, Canada, Jordan, Kuwait, and India that traveled to Iran for doing sports competition (N=132). The sample size was considered equal to the population. Descriptive and inferential statistics methods for data analysis were used. The results showed that according to the mean of tourism components, culture and infrastructure components in order of priority are effective in reducing active sports tourism in Iran. The research findings on the importance of cultural and structural as factors affecting attracting sport tourism have stressed. Thus, it is recommended that sport managers adopted an appropriate strategic planning to increase sport tourist
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".