VIRTUAL REALITY AS A RESPONSE TO EMERGENT CHALLENGES IN ARCHITECTURAL EDUCATION
Bibliographic record
Abstract
Though Virtual Reality (VR) has become a ubiquitous medium in a diversity of entertainment media, it has remained an esoteric platform for academic use. Digital technologies have become primary means of quotidian life ranging from online forms of entertainment (including video games and streaming media) to online shopping. The COVID-19 pandemic dramatically accelerated digital pedagogy at all levels of education, most notably in the post-secondary market, and specifically within the study of architecture. Architectural praxis has been generally quick to adopt technological innovations in the production and outputs of the built world, however it has been niche and its widespread adoption and standardization has been inadvertently challenged by a lack of unifying goals across all architecture organizations from academia through to professional practice. The pandemic has served as a tipping point in the advances in architectural education, specifically through the medium of VR. With its ability to immerse users in completely different environments and bring them together in a shared virtual world, VR is poised to rapidly become a commonplace venue for design education. From serving as a presentation platform to a developmental collaborative medium, VR is a robust, interactive platform that will become a part of the "new normal" long after the pandemic similar to the rapid and extensive adoption of videoconferencing tools during the pandemic.
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 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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".