Learning From The Redesigned : Takeaways From Unauthored And Emergent Spaces
Bibliographic record
Abstract
This thesis investigates the phenomenon of "redesigned spaces," informal environments that emerge in response to unmet human needs, shaped and defined by the people who inhabit them. Unlike common architectural typologies, redesigned spaces are fluid, and dynamic, arising from a blend of resilience, necessity, and creativity. Through analyzing case studies in the UAE, East Los Angeles, and Hong Kong, this research explores how diasporic communities transform underutilized urban spaces into vibrant social and cultural hubs, creating subcultures that are neither entirely imported nor assimilated. The study identifies key components of redesigned spaces: the built environment as a neutral canvas, catalysts that activate use, and the diasporic contexts that influence form and function. It highlights their ephemeral, adaptable nature and their ability to fulfill complex human needs that traditional architecture often overlooks. Finally, a speculative fictional sequence is used to examine how an abandoned site in Vancouver might be occupied and transformed over time. By simply removing barriers and fences, spontaneous activities might emerge, exposing how public space functions beyond formal regulations. This study highlights the value of observing unplanned urban dynamics, questioning how existing by-laws shape, limit, or enable meaningful interactions. Ultimately, it proposes a new tool for architectural design approaches-one that learns from, rather than imposes upon, the organic evolution of space.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".