Abu Dhabi, Doha and Dubai: Going online – a Comparative Analysis of their Visibility on a Booking Website
Bibliographic record
Abstract
The aim of this article was assess the visibility of the three cities of Abu Dhabi, Doha and Dubai on the internet on the basis of data collected from a booking platform. The internet has revolutionised the way humans and machines interact, the way humans advertise and market their goods and services including tourism products. This article focuses on the accommodation sub-sector. The article was compiled using primarily secondary data available on the internet. No primary data was collected. The findings reveal that Dubai is relatively stronger as compared to the other two cities, purely on the basis of available accommodation facilities in which it showed preponderance on the basis of available rooms. It emerged as a city strong in architecture, shopping and entertainment as well as desert safaris. While closer to Doha on the parameters investigated, Abu Dhabi finds itself in the middle between the other two cities in relation to accommodation. It is strong in respect to high-profile sporting events, and emerges as an excellent cultural/entertainment city. Doha came out in the findings as a city ideal for relaxation and beachfront activities. The article suggests that destination entities like tourism authorities must find ways to use Online Travel Agents (OTAs) such as Booking.com to popularise their facilities and destination for visibility.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".