RECONSTRUCTION OF ALEPPO, SYRIA: A DESIGN STUDIO AT THE UNIVERSITY OF NOTRE DAME, USA
Bibliographic record
Abstract
As we think about how to rebuild communities that have suffered destruction, it is imperative to discuss the recovery of architecture and culture.As the world of architectural education trends towards teaching the design of buildings that create "placelessness", we must teach students to look deeply at history, to synthesize its lessons, and apply them sensitively and appropriately.Aleppo in Syria has a rich heritage of Islamic architecture that spans millennia.While the destruction of Aleppo has left a scar in the hearts of its people, it is possible to rebuild using the lessons and forms of traditional architecture.At the University of Notre Dame, our current studio of fourth year students is studying and proposing how a neighborhood in the historic city center might begin to be rebuilt.This studio is a teaching tool that allows students to engage with a culture that is foreign to them; to see and apply universal principles adapted for climate, culture, and building technologies that have been honed locally over thousands of years.The core of the studio is analysis of traditional Syrian architecture, looking at how the architecture supports the community and engages with the unique climate.The students study how traditional building technologies, like dome and vault construction, support the functions housed within these forms.They delve into the distinguished history of craft as they study architectural ornament.All these lessons are ultimately applied to a building project, which they program, conceptually develop, and design at the scales of the urban, building, and detail.These students, while they may be practicing far away from Aleppo, are preserving the heritage of a place that has seen so much destruction.Their work is a testament to the power of design in recovering the memory of a city.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 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".