Urban regeneration and building retrofit. A strategy towards instilling a culture of innovation and entrepreneurship
Bibliographic record
Abstract
Abstract The United Arab Emirates’ economy is transitioning to a knowledge-based economy by promoting innovation and research development. Supporting the UAE’s Vision at becoming among the best and most innovative nation in the world by 2071, the Government has developed frameworks that recognize the importance of innovation to an economy’s growth and development. This paper presents the results of a design research where the domain of architecture and engineering blend with economics and social studies to the serve the UAE’s vision, proposing urban solutions to launch the country in its ‘next 50’ years, with an eye for the preservation and revitalization of the exiting and valuable resources. The research project proposes a different geography of innovation and introduces urban regeneration strategies to stimulate innovative policies for the built environment of the entire UAE territory. With the intent of forming an intangible connection between the seven Emirates, the proposed intervention can be situated in every state. The study especially looks into the three neighboring Emirates or Dubai, Sharjah, and Ajman, and finally select the latter to test the introduction of strategically designed spaces in degraded (and disconnected) locations to encourage the community to innovate while at the same time reusing/refurbishing the existing resources/buildings/facilities. The specific case study involves the design of an incubator facility in an obsolete villa community in Ajman, formerly hosting locals (therefore luxurious) that now have left for better locations and cannot manage to resell their properties due to the decadence of the neighbourhood. The incubator, a building articulated in the interstitial spaces in between the villas, would reactivate the district by attracting young and innovative entrepreneurs, who settle there for both working and living, exploiting the incubator complex as a parasite of the existing villas. If successful, the project will revive the district, provide it a new brand, and create a new financial stream to self-support its gradual regeneration.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".