Bibliographic record
Abstract
A long history of media experimentation from amusement parks to mechanical stagecraft has helped define what we now call virtual production. In 1985 the CN Tower in Toronto unveiled Tour of the Universe that used custom-built virtual media within a ride design for the first time. Rides of the early 2000s used advancements in computer game technology to create complex three-dimensional simulations for users. Universal’s Spiderman and Transformers rides exemplified the use of hybrid three-dimensional design that would form the blueprint for virtual production in cinema. Despite the role of ride design in the evolution of virtual production, little is written about how its influence might be shaping creative processes in cinema. Virtual production has the capacity to emulate real-life three-dimensional scenes, but this comes with limitations. Virtual windows and doors are impermeable, and spaces must be designed to particular scales and dimensions. This chapter examines the creative potential and limitations of virtual production in the ride industry and how it could inform more inventive uses of the technique in cinematic production. The study contains an overview of the history of virtual production in ride design followed by a series of interviews with creative practitioners working with cinematic narratives for ride audiences. Interviews include David Cobb, PCD, Thinkwell (Twilight, Men in Black), Emmy Award winning Virtual Director Stephan Grambart (Sleepy Hollow) and virtual production expert and Worldbuildr CEO Michael Libby. This research offers a clear definition of what virtual production is and highlights the importance of ride culture in understanding its applications across all creative industries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 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".