CAPPADOCIA VISITOR PROFILE ANALYSIS : POST-CRISIS CHANGE AND ITS DYNAMICS
Bibliographic record
Abstract
Cappadocia which is both UNESCO cultural and natural heritage area is in 32 (3%) very rare area in 1031 UNESCO heritage sites. It is an important destination with high potential product diversity in the field of natural, historical and cultural circumstances. On the other hand, destinations' performances do not just depend on the feature of destinations, but also the macro-criteria (such as security or transportation capabilities) of related countries. To be able to make segmentation in terms of marketing mix theory, analyzing the visitor profile has crucial role. Measuring changes on market provides decision makers to make root cause analysis and put forward which countermeasures should be taken/developed. In this perspective, aim of this study is to classify the visitors stayed in Cappadocia country, term and stayed nights basis and to determine the factors (and effect levels) affecting their travel choices in a macro environmental perspective between 2011 and 2015. The secondary aim of the study is to figure out the long-term relationship among countries' travel behaviors to Cappadocia considering stochastic trend. For these aims, cluster analysis is done for objective classification. The factors (and affects level) that affect visitors' travel are figured out via setting panel regression model. In addition, cointegration analysis is used to figure out the long-term relationship among visitors. Results show that while Germany, France and Turkey had unique visiting time pattern which means that they all have a specific visiting behavior, European Union countries had medium sized similar strength on Cappadocia travel. And Canada, Hong Kong, France and Japan had long term similar visiting pattern. Finally, fixed affect panel regression analysis results present that GDP is the only significant variable that affects visitors' visiting behavior for Cappadocia.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".