Off-Season Tourists and the Cultural Offer of a Mass-Tourism Destination: The Case of Rimini
Bibliographic record
Abstract
This paper assesses the potential implications on off-season tourism of enhancing the cultural offer of Rimini, a popular Italian seaside holiday destination hosting about 12 million overnight stays per year. Since more than 9 million of these stays are concentrated in the summer season, in the last 20 years. Rimini has been undergoing a policy of seasonality smoothing, which mainly pivots around business and cultural tourism. This assessment has been carried out through discrete choice experiments submitted to a sample of about 800 tourists who visited Rimini outside the summer months. Since tourism can be viewed as a composite good, which overall utility depends on how the component characteristics are arranged, the choice experiments allow to disentangle the importance and the willingness to pay of tourists for different attributes of the holiday. The choice model incorporates a number of possible changes to actual tourism features (which are also the subject of public debate), including them in hypothetical alternative “holiday packages”. The conditional logit analysis of the choice experiments can highlight any synergy or trade-off between cultural and business tourism. Results suggest that business and leisure tourists share many features related to the use of the territory, while there are important trade-offs between these two groups and cultural tourists. Since business tourists have a higher willingness to extend their stay, a softer budget, and their demand is also complementary to the demand of summer tourists (Brau, Scorcu, & Vici, 2009), from the destination point of view investing in this market segment would be the best option. Although a “second best”, however, cultural tourists share with the local population of Rimini many aspects of the demand of territory (Figini, Castellani, & Vici, 2009). Hence, cultural tourism can play a fundamental role in the intermediate season as a tool for smoothing seasonality, to diversify investments and to give value to the city’s cultural heritage.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".