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Record W4240756811 · doi:10.32920/ryerson.14647260

Corporeal architecture: a material response to sensorial experience

2021· preprint· en· W4240756811 on OpenAlexaff
Michael P. M. Blois

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArchitecturePerceptionVisual artsFocus (optics)AestheticsSociologyWork (physics)ArtEpistemologyEngineeringPhilosophy

Abstract

fetched live from OpenAlex

My project began with an interest in sensory experience and the means by which the body engages in architecture. Numerous threads were explored- studying the work of Aalto, Scarpa, Holl, Zumthor and the writing of Pallasmaa; examining the work of artists like David Rokeby and Michael Awad; research about perception and sensation, through Deleuze, J.J. Gibson, Frampton, Frascari...And through personal experience: documenting sites in the city through different seasons, visiting the American Folk Art Museum (and others) in NYC. The project developed into a critique of the critique, referring to the ocularcentric critique. This critique argues that vision has been the focus of architects and designers at the exclusion of the other senses. This critique is a point of departure for my work, which seeks to add a new layer--through a study of the links between the senses (intersensorality) as they occur in the experience of architecture. I have identified a number of key moments in architectural experience that highlight these links and provide a venue for experimentation (moments when a number of the senses are at play). Together with a number of threads and supporting ideas, the design portion of the project tests materials, forms and conditions in order to bring the links between the senses into focus. This design research is contained within my proposed, small addition to an existing branch Library on Queen Street West.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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.

Study designSimulation or modeling
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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