Speaking Culture: Language, Identity, and Tourism in Saudi Arabia's Vision 2030
Bibliographic record
Abstract
Language is important in every social context, as through language people communicate, interact and express oneself. At the same time tourism plays an important part in shaping the perception of self through experiences of other people and places. This study explores the intersection of language, identity and tourism under the Saudi vision 2030. It explores the perspectives of international tourists, Saudi locals from major cities, and rural areas to understand the evolving linguistic landscape and its influence on the Saudi people, tourists and the tourism experience in general. The findings reveal that language plays a fundamental role in shaping and expressing one’s cultural identity and hence influencing tourists’ experiences. They also illustrate the importance of language as a source of authenticity and a means of cultural engagement. They reveal complex dynamics, with international tourists highlighting the challenges of language barrier and at the same time admiring the beauty and authenticity of Arabic language and the role this language played in their overall experience. On the other hand, local Saudis express a range of views on the role of Arabic and English in tourism and national identity, as some admitted that while speaking a very good English, they preferred to communicate with tourists in Arabic to enrich their experience and to show another part of their culture. Moreover, photographic evidence further explores the different linguistic patterns in rural areas and major cities and provides more insights into the effects of such bilingual practices on tourists and citizens. This study contributes to understanding the role of language use on the tourism industry and how it can enhance cultural exchanges. It also contributes to the understanding of the sociocultural implications of Saudi Arabia's vision 2030 and its ambitious developmental goals and provides insights for policymakers.
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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 | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".