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Record W3211691135 · doi:10.32920/ryerson.14649720.v1

Designing for Complexity: A Systemic Approach to Spatial Relationships in High Density Housing

2021· preprint· en· W3211691135 on OpenAlexaboutno aff
Shiloh Lazar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureFunctionalism (philosophy of mind)SociologyArchitectural engineeringNormativeMateriality (auditing)Transparency (behavior)AestheticsEconomic geographyComputer scienceCognitive sciencePolitical sciencePsychologyGeographyVisual artsEngineeringLawArtComputer security

Abstract

fetched live from OpenAlex

The functionalism and reductivism behind post war modernist high-rise housing typologies like the slab block, failed to understand the impact of this highly condensed circulation on the social interactions of residents. Contemporary high-rise architecture typologies like the point tower still don’t account for the complex social needs of inhabitants - providing isolated group activity spaces in lieu of addressing and elaborating the shape and form of the transitional spaces between the street and the unit door. This thesis asserts that understanding the complexity of social needs and normative social behavioral patterns will inform an approach to design that will allow for a more humane and socially interactive environment. This thesis design explores Systems Theory, Pattern Language, recent precedents and tactics like clustering, layered gradients of privacy, visual buffering, transparency, texture and materiality in a high-density residential design for Toronto’s rapidly intensifying core.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.033
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.085
GPT teacher head0.230
Teacher spread0.146 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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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