Establishing a cultural quarter in Abu Dhabi, UAE
Bibliographic record
Abstract
The main argument driving the thesis; namely the fact that the literature is treating cultural quarter development as mainly originating from the supply side with limited attention to the demand side (e.g., consumers' /visitors' preferences and tastes for prospective developments in the cultural scene). Thus, the analysis argues that more attention should be placed upon cultural quarter establishment, as opposed to cultural quarter production. For this purpose, the thesis engages into a quest to reveal individual preferences for future policy initiatives in the area. The thesis considers the case study, namely the proposed cultural quarter development in Saadiyat Island, in Abu Dhabi, United Arab Emirates (UAE). It provides a background on Abu Dhabi, the reason for focusing on cultural tourism, the Saadiyat island development and the planned activities' and cultural infrastructure to appear on the island. The thesis also considers the rationale behind . the project (i.e., diversification of the Emirate's economy) as well as the potential criticism that may arise (has risen as a result of this policy initiative) in the literature. The main argument in favour of this policy initiative is the diversification of the mono-culture nature of the domestic economy, whereas the main argument against such an initiative is that it will appear as an 'elitist' development not catering for native culture and tradition, thus failing to relate to native customs and cultural heritage. We argue that this issue will also have negative repercussions for the sustainability of the cultural quarter if not addressed properly from policy makers.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".