Architecture & Legitimacy: Strategies for the Development of Urban Informal Settlements
Bibliographic record
Abstract
Currently, 1 in 6 people live in slums, or informal settlements in cities throughout the developing world. They are built illegally and are characterized by lack of proper sanitation, unsafe housing, and crowded living conditions. Despite their appearance, informal settlements are legitimate communities; they are vibrant, with sophisticated social, economic and cultural networks that support the livelihoods of residents who call them home. These communities give the urban poor a physical place within the city, giving them access to the opportunities and advantages that the current age of the 'global city' can offer to any willing participant. As architects who see the responsibility in choosing the informal settlement as a realm for engagement, this thesis proposes that any architectural intervention be mindful of the importance of the networks contained within the streets and buildings of the informal settlement. By preserving the built-fabric of the settlement, the architect legitimizes the settlement's density and scale, while ensuring the urban poor have a physical place in the city. They have managed to develop their own communities without any investment from outside forces, any intervention should only support that autonomous development. These structures, as well as the people and activities with them, are vital to the survival of residents of informal settlements.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.043 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".