MIXED REALITY MEDIA TECHNOLOGIES AS A CATALYST FOR ARCHITECTURAL AGENCY
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".