A New Kind of Videoludic Presence: How VR Is Breaking the Fourth Wall
Bibliographic record
Abstract
Abstract This chapter explores how virtual reality (VR) distinguishes itself in terms of immersion within the field of video games. Central to this inquiry is whether VR represents a revolutionary new gaming paradigm or merely the latest medium falling short of gamers’ expectations. The discussion begins by examining the concepts of immersion and presence, highlighting their overlapping properties and how VR's capability to merge perspective and interaction enhances these experiences. Immersion involves sensory stimulation, narrative attachment and player agency within virtual environments (VEs), while presence is defined as the sensation of inhabiting a virtual universe. The chapter addresses VEs, diegesis and fictional worlds, building on the narratology and ludology debate. It chapter assesses the roles of environmental storytelling and the world inhabiting effect in games, particularly open-world role-playing games set in fantasy and science fiction universes. These games offer players active participation in narrative construction, enhancing immersion. Furthermore, the analysis compares third-person and first-person points of view (POV) in flat-screen games with VR's unique integration of POV, point of action and point of interaction. VR's intuitive interface fosters a profound emotional response and a heightened sense of presence, breaking the fourth wall and blurring the line between gameworld and reality. This chapter concludes that while VR offers a distinct form of representation, it does not constitute a new gaming paradigm yet. However, advancements in the medium may soon bring VR closer to the immersive experiences envisioned by Janet Murray's holodeck.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".