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Record W4402199478 · doi:10.32920/26866558

The Walls Talk Back: Fostering a Dialogue Between Architecture and Its Inhabitants Within Multi-Family Housing

2024· preprint· en· W4402199478 on OpenAlexaboutno aff
Jeannette Wehbeh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureSociologyArchitectural engineeringBusinessPolitical scienceGeographyEngineeringArchaeology

Abstract

fetched live from OpenAlex

Inhabitants and their use of space within the home is in constant flux, changing temporally in relation to dynamic factors such as an individual's age, a household's size, daily needs and activities, the time of day, and weather conditions. Contemporary multi-family housing projects in Toronto are not designed to support these changes as units are often too small and are segmented into rooms with fixed uses, contributing to inhabitant dissatisfaction. In this thesis, the question of adaptability was explored to promote wellbeing and improved space usage. Research began with literature reviews and case study analyses which established design parameters specific to adaptability in multi-family housing. A project sited within Toronto was developed as a critique of contemporary design methods. This thesis reimagined the role of walls as a way of initiating continuous dialogue between inhabitants and architecture in the design of spaces that satisfy each individual's needs over time.

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)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.021
Scholarly communication0.0090.005
Open science0.0010.012
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.061
GPT teacher head0.316
Teacher spread0.254 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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