Beyond the Screen: The Integration of XR Media in Canadian Cultural Institutions
Bibliographic record
Abstract
The integration of Extended Reality (XR), encompassing Virtual Reality (VR) and Augmented Reality (AR) represents a paradigm shift in contemporary art and media landscapes. While XR permeates sectors ranging from gaming, real estate, advertising to education, its foray into cultural institutions like film festivals and artist-run centres (ARCs) remains an underexplored research area. Furthermore, existing literature often examines the technological facets of XR, overlooking its cultural impact. \n\nAddressing this gap, this dissertation is a comprehensive exploration of how Toronto's cultural institutions are embracing XR, their strategic adaptations to it, the challenges encountered, and the ensuing ramifications on audience dynamics and institutional ethos. Through case studies, my research examines film festivals’ use of XR, particularly within TIFF, Hot Docs, and imagineNATIVE, the notable Art Gallery of Ontario, and pivotal ARCs including Trinity Square Video and Inter/Access.\n\nUsing a sociocultural framework, this dissertation meets and probes at the nexus of technology, artistry, institutional imperatives, access, and audience interactivity with XR. By offering insights into film festivals’ engagement with XR, emphasizing its influence on festival operations, labour and programming. The examination then shifts to the art gallery, with a spotlight on the AGO, unraveling the tensions and trade-offs of blending legacy with XR innovations. The role of ARCs takes center stage as incubators for XR experimentation and platforms for artist empowerment. This dissertation culminates in a critical discussion of the challenges posed by the technological and planned obsolescence of XR artworks in a capitalist market. It advocates for sustainable methodologies to safeguard the longevity and accessibility of such works and uncovers how artists are addressing the concern of obsolescence within their XR artistic practice. \n\nBeyond mere technological enhancement, the integration of XR by cultural institutions intertwines with complex sociocultural, economic, and artistic nuances. This dissertation highlights the seminal role of cultural institutions in defining XR’s trajectory in the arts, making it a critical read for cultural curators, artists, scholars, and policymakers grappling with how to manage the pace of change in the emerging media landscape.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
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".