Exploring <i>The Library at Night</i>: A Conversation about Robert Lepage’s VR Experience in Toronto
Bibliographic record
Abstract
In this forum piece, three scholars from different disciplinary backgrounds discuss their experience of the Toronto run of Robert Lepage’s The Library at Night, an immersive virtual reality (VR) experience. The show’s Toronto debut was produced by Lighthouse Immersive in spring 2022. Taking their cue from Shannon Jackson’s discussion of how disciplinary training and framing impacts perceptual habits (2–4), the authors explore how their various lenses shape their understanding of the multidisciplinary and difficult-to-categorize experience. They discuss affect, audiences, artistic choices, and the show’s larger context. Examining how the show constructs an aesthetic experience for its audience, and critiquing its limited epistemological frame, they consider how The Library at Night fits within our current moment of experience economy in the arts and culture industry.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.062 | 0.032 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| 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".