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Record W4238470513 · doi:10.32920/ryerson.14654034

Architecture & Legitimacy: Strategies for the Development of Urban Informal Settlements

2021· preprint· en· W4238470513 on OpenAlexaff
Rachel D. Pressick

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSettlement (finance)LivelihoodHuman settlementSanitationIntervention (counseling)Informal settlementsUrbanizationLegitimacyEconomic growthArchitectureRealmBuilt environmentBusinessEnvironmental planningGeographyPolitical scienceCivil engineeringEngineeringPoliticsFinance

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.069
GPT teacher head0.328
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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