Bibliographic record
Abstract
Tall buildings, by definition, are vertical objects. Historically, architects are more concerned about the tops of towers and less about their bases. Understandably, this is to make a statement through which more attention to the building can be drawn. However, the building base”the podium”is the place that is important to ground the building within its context. This neglected part of tall buildings is responsible for not only welcoming people to this gigantic structure, but also mediating the scale of the tower with the surrounding buildings and creating a good public realm for the city. This paper aims to address the issue of urban integration between tall buildings and the urban fabric. To achieve this goal, a desk study and field work were undertaken. The former involved a literature and professional documents review, whereas the latter involved interviewing 23 experts from the Gulf Region (including architects, planners, and academics) and observing six tall buildings in the Gulf Region's main cities, including Riyadh and Dubai. This study draws some lessons from comparing tall buildings in the Gulf Region with those in Canadian cities, including Toronto and Calgary. This paper concludes with proposing some design recommendations to improve urban integration, enhance the quality of ground spaces in tall buildings, and refine our experience with the podium. KEYWORDS: urban integration, public realm, tall buildings, vibrant places, tower podium
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 | 0.001 | 0.003 |
| 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 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".