Chinese tourists' on site experiences in Florence: applying the orchestra model
Bibliographic record
Abstract
[Extract] Abundant evidence exists that Chinese tourists are traveling in every-increasing numbers outside of Asia (China Tourism Academy, 2014). In particular, substantial numbers of both independent and group tourists are now visiting Europe (Arlt, 2013; Lai et al, 2013; Wu and Pearce, 2014). italy has become a prominent destination for these new waves of visitors. Remarkable growth has occurred in 4 regions: Lazio (where the capital city of Rome is located), Lombardia (with Milan as its central city), the Veneto (where Venice is the popular city destination), and Tuscany (with Florence as its feature city). In 2013, these 4 regions hosted almost 50% of Italy's 538,000 Chinese visitors (CaixinOnline, 2014). For Tuscany, with Florence as its capital, China is now the fifth most important non-European market after the United States, Japan, Canada , and Australia (Ministero Affari Esteri-Agenzia Nazionale del Turismo (MAE-EMTI), 2012). As research on Chinese tourists grows in the Western academic literature, it becomes important to provide detailed information on how the rapidly growing Chinese market engages with pivotal destination (cf. De Carlo et al., 2009; Woodside et al, 2007). There is a major need to understand the rich reatcions of Chinese tourists to the key locations they visit since such studies address tourists' wel-being and offer guidelines for distination managers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".