Visitors and Residents in El Raval Neighborhood of Barcelona. New Opportunities for Creative Tourism?
Bibliographic record
Abstract
This article analyzes the relationship between creative tourism and intercultural interaction. The research took place in Barcelona, a city that has become, during the last three decades (1990-2020), a renowned international urban destination. El Raval, a central and multicultural neighbourhood, is the most serious example of a neighbourhood in the city that has experienced rapid tourism growth and pressure. Given the city’s wholesale adoption of the co-creation of place, some of the criteria of creative tourism experiences have been used to determine a baseline of engagement attitudes and behaviours of residents and visitors in El Raval neighbourhood. A special emphasis has been given to the role of social media, and how it might affect the relationship between residents and visitors from a creative tourism point of view. The authors created a specific survey which was distributed online to residents and visitors. The results of this study show different perceptions between residents and visitors. On one hand, residents are less willing to engage in the creative tourism enterprise than are visitors. On the other hand, residents underestimate the interest of visitors in connecting with them, while visitors overestimate the interest of residents in connecting with them, suggesting that communication is something that can be improved. Those results make evident the need to use and develop social media tools to connect residents and visitors, and promote cross-cultural interactions and creative tourism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".