MétaCan
Menu
Back to cohort
Record W4312677244 · doi:10.1145/3551708.3556203

MIXED REALITY MEDIA TECHNOLOGIES AS A CATALYST FOR ARCHITECTURAL AGENCY

2022· article· en· W4312677244 on OpenAlexaffabout
Vincent Hui, Alvin Huang, Ariel Weiss

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAugmented realityAgency (philosophy)Mixed realityVirtual realityExhibitionExperiential learningCapstoneEmerging technologiesArtificial realityAccreditationArchitectureEngineering ethicsPresentation (obstetrics)EngineeringSociologyComputer sciencePolitical sciencePedagogyComputer-mediated realityHuman–computer interactionVisual artsSocial scienceArt

Abstract

fetched live from OpenAlex

Though incredible strides have been made in the adoption of mixed reality technologies in the past decade, the reality is that they have often remained inaccessible for a variety of reasons including expense and expertise. While both the costs and learning curves of these technologies have flattened in recent years, they remain an esoteric component in contemporary pedagogy. This is fundamentally due to the lack of curricular and extracurricular application. This presentation demonstrates the effective adoption of virtual and augmented reality technologies in Canada's largest undergraduate accredited architecture program that has given agency to students in their academic design work and more noteworthy, their extracurricular initiatives. Through a comprehensive case study of a student project showcased at the recent international Winter Stations design exhibition, this paper demonstrates the effective inculcation, integration, and application of mixed reality tools in empowering students to bring their design ideas to built reality. The integration of innovative mixed reality technologies is no longer hampered by technological accessibility; it is mired by curricular inertia and dogma. The promise of breaking through conventional pedagogical frameworks with innovative technologies is reinforced and highlighted in this experiential learning precedent.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.046
GPT teacher head0.283
Teacher spread0.237 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicAugmented Reality ApplicationsFrench-language works237,207