Expanding Space: Redefining Persian Cultural Hubs in Toronto through Interactive Architecture
Bibliographic record
Abstract
Persian communities in Toronto use certain \npublic spaces as a group. One of the less obvious, \nbut interesting, spaces that serve as cultural hubs \nfor these communities are strip malls. These cultural- \ncommercial spaces sometimes act as spaces \nfor political protest or cultural gatherings. This \nobserved use of space was the basis of this thesis \nand formed the initial thesis question: “how \ncan we start thinking of the strip mall as a public \ngathering space?” In seeking the answer to this \nquestion, there were challenges to face. The nature \nof strip malls is that they sometimes act as a \npublic space and sometimes as a parking space. \nIn order to expand the public uses of these kinds \nof hubs, space needs to be shared between cars \nand people. This thesis therefore seeks to explore \na dynamic (interactive) architecture that can be \nexpanded based on the different conditions of the \nsite (strip mall). \nThis thesis aims to study and employ three different \nelements to create prototypes that have the \ncapacity to expand limited space in this context: \nthe strip mall’s spatial qualities, deployable techniques \nfor creating a dynamic space, and Persian \nculture. Among several models produced using \ndeployable techniques, two prototypes have \nbeen developed as the most appropriate models \nfor expanding the public space in a Persian context. \nThese are the “Market Shell” and “Gathering \nShell.” When there is a cultural or social occasion \nafter business hours, these shells activate the \nstrip mall through the functional space that they \nprovide. However, during opening hours, these \nshells are deactivated to permit the stores and \nparking area of the strip mall to function.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".