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Record W4296733816 · doi:10.32920/ryerson.14647458.v2

Redressing the Ocularcentric Culture of architectural design: A Phenomenological and Participatory Approach in VR

2022· preprint· en· W4296733816 on OpenAlexaff
Elisaveta Boulatova

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsExperiential learningPerceptionVirtual realityRepresentation (politics)ArchitectureContext (archaeology)Computer scienceHuman–computer interactionPsychologyAestheticsMultimediaVisual artsMathematics educationArt

Abstract

fetched live from OpenAlex

In the development of architectural representation, the image has become a metonym of architectural experience. This productive tension is maintained by the expectation of fragmented representation to identify and reproduce the experiential and phenomenological dimensions perceived in the physical experience with architecture. Returning to the investigation of representation and perception exemplifies that each new technology introduces a new scale of extension in both design capability and opportunity for feedback. The degree of immersivity introduced by virtual reality technologies elicits several scales of experience. This thesis uses virtual prototyping in the design stage to elicit a product that is not a reflection of intention, but a result of something discovered, evaluated and experienced in immersion prior to realization. An increase in receptivity to the qualities of the design and its context relative to the perceiver has the potential to change the relationship of digital space to digital place.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.749
Threshold uncertainty score0.501

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 integrity0.0000.001
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 head0.060
GPT teacher head0.255
Teacher spread0.195 · 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 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
Published2022
Admission routes1
Has abstractyes

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