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Record W4244128616 · doi:10.32920/ryerson.14646042

Towards a “Mall-Less” Suburb

2021· preprint· en· W4244128616 on OpenAlexaff
Stephen T.C. Wong

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNeighbourhood (mathematics)ArchitecturePublic spaceSpace (punctuation)Shopping mallSquare (algebra)Private spaceSociologyPhenomenonPublic relationsBusinessEconomic geographyGeographyPolitical scienceAdvertisingArchitectural engineeringEngineeringComputer scienceArchaeology

Abstract

fetched live from OpenAlex

The creation of suburbs has led to segregated functional spaces that once helped nourish their growth, including the shopping mall. However, as the shopping phenomenon evolves, it is crucial to reexamine this building type to accommodate changing needs and their effect on the surrounding area. Shopping malls have been important contributors to social life and through transformation, street and square life can be recreated for suburban communities. This thesis explores the formation of a square within a mixeduse neighbourhood that intertwines public and private spaces to create opportunities for social connectivity. This enhances the existing community and strengthens the relational benefits between public and private realms, and strives to establish a balanced growth. Architecture mediates public and private spaces, and redefines boundaries so that underutilized private space can be optimized and reintegrated into the suburban environment, reestablishing the social life in the suburbs.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.021
GPT teacher head0.204
Teacher spread0.183 · 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 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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