Bibliographic record
Abstract
In recent years numerous innovative and affective virtual reality (VR) works combining documentary-based or fictional storytelling with game mechanics, live theatre and other elements, have appeared at festivals or on online distribution platforms. These interdisciplinary works have much to tell us about the future of VR storytelling but have yet to receive sustained analysis. This book aims to correct that. The monograph delves into recent evolutions in VR storytelling, focusing on entertainment-based works created or launched since 2020. It features 8 chapters that delve into VR formats with various levels of interactivity, from 360-degree videos to open world VR games. Through an analysis of case studies, the chapters showcase the increasing diversity and sophistication of recent narrative-based projects. Moving past the initial hype associated with the latest wave of VR, the book aims to explore the specificity of a story delivered in this medium considering narrative structures and approaches to narration. Dooley argues that VR, as an interactive medium that places the user inside a story world in a visible or invisible virtual body, offers narratives that incorporate the user’s body as a storytelling tool. This fosters user-centred stories that unfold in three-dimensional space. Adopting phenomenological and formal analysis methodologies, the monograph examines case studies through their approaches to narrative, style, and interactive devices. Key concepts that are explored include agency, direct address, environmental and spatial storytelling, embodiment and presence. By providing a much-needed analysis of works through a variety of theoretical lenses, the book illustrates how recent VR storytelling fosters powerfully transformative experiences.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".