Abu Dhabi and Doha: Skyscraping for Tourism Development
Bibliographic record
Abstract
This article explores the link between the proliferation of skyscrapers and hotel establishments. In the Middle East, particularly in Qatar, the skyline is replete with tall buildings that serve multiple functions: hotels, residences, offices, and combinations of these. This article uses relevant secondary data to analyze how Doha and Abu Dhabi transformed their built environment through the construction of skyscrapers as a symbol of their wealth, of their participation in the world's informal skyline competition and their national pride. Tourism growth in these two cities—both of which have invested in urban planning and have made deliberate efforts to develop tourism as a means of diversifying an oil-based economy—has been phenomenal. Skyscrapers have become a tourist attraction in themselves, and tourism growth has spurred the construction of more skyscrapers for multiple purposes. Thus contemporary architecture has transformed these cities into world-class tourist destinations. Heritage and contemporary architecture arguably have equal power to attract tourists. By using architecture to influence tourism outcomes, Doha and Abu Dhabi have succeeded in transforming themselves into formidable global players in tourism, offering not only the traditional three S's (Sea, Sun, and Sand) in the Emirates but also skyscrapers and shopping as attractions. The article recommends that a skyscraper should ideally be multi-purpose in order to cater for a wide variety of client needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".