DETERMINING THE MODEL OF TOURISM BUSINESS DISTRICT (TBD) IN COASTAL RESORTS: A CASE STUDY OF TURKEY
Bibliographic record
Abstract
Coastal resorts, whose dominant economic activities are those of providing an array of recreational services to tourists, reflect this specialization in their land-use patterns. Therefore, the business districts in coastal resorts have a unique morphology, landscape, and land use. However, the literature reflects that there is limited attention to the tourism business districts (TBDs) that have developed in coastal resorts. Moreover, few empirical studies have been conducted in developing countries, such as Thailand, China, and Turkey, as well as developed ones such as United States, Canada, and Italy. This study discusses the TBDs located in Turkey’s coastal resorts in terms of location, form, and function. The findings are presented statistically, and detailed maps are presented to explain the TBDs from a geographical and practical perspective. In this study, ArcGIS 10.5 software has been used to perform spatial analysis of the data. The main findings include that Turkish TBDs have similar characteristics in terms of location, form, and function compared to other coastal resorts worldwide. Therefore, it is possible to say that these similar features constitute a model in terms of land use. In addition, the statistical findings of the study are largely similar to those found in the literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".