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Record W3118445557 · doi:10.51508/intcess.202133

EXTENDED REALITIES AS METHODS OF REPRESENTATION WITHIN ARCHITECTURAL PEDAGOGY

2021· article· en· W3118445557 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of INTCESS 2021- 8th International Conference on Education and Education of Social Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)Computer scienceProgramming languageHuman–computer interactionPolitical science

Abstract

fetched live from OpenAlex

Although various extended reality (XR) technologies share origins in entertainment, the medium has warranted integration within a range of disciplines, most notably in architectural praxis and pedagogy.In the past, immersive technologies have become synonymous with architectural representation.Previously, XR tools were confined to the visualization of final outcomes, however with increasingly robust software and hardware, they have begun to cascade into other developmental processes and phases.In recent years, there has been a strong push in academia to incorporate immersive experiences into development and idea iteration processes, representation methods, and media for instruction.Such tools are not only able to improve architecture student's abilities to understand the spaces they design digitally in a more comprehensive manner but they are also able to provide extensive insight into existing and historical architectural projects, allowing students to gain a more complete understanding of the built environment.This paper re-examines the AEC industry's relationship with various immersive media and the role these XR technologies play within architectural development and processes.It will begin with defining and distinguishing various XR technologies, including virtual reality (VR), augmented reality (AR) and mixed reality (MR), through a literature review.Through a series of case studies within both architectural pedagogy at Canada's largest architecture program and the professional industry at large, this paper will posit not only the changes in the purpose of immersive technologies in architecture, but also outline the merits for their use within contemporary architectural pedagogy.The paper concludes with projections on the future role of XR platforms within the context of architectural pedagogy.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.466

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 head0.077
GPT teacher head0.431
Teacher spread0.354 · 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