Bibliographic record
Abstract
Since 2014, a new narrative medium has been emerging that is delivered through virtual reality headsets. This paper theorizes narrative virtual reality as a new filmic medium with new and unique affordances that facilitate narrating and sharing memories. In contradistinction to the much-discussed concept of VR as an ‘empathy machine’, we propose to think of VR as a memory machine. This capability of VR is reflected in the proliferation of memory-related content in narrative VR and its early adoption in public history. Narrating and sharing individual and collective traumatic memories have featured prominently in narrative VR. Three unique properties enable a recipient of narrative VR, the immersant, to experience someone else’s memories: embodiment, immersion, and interactivity. First, embodiment allows the immersant to assume the role of the person who remembers or a person that is being remembered. Second, immersion enables the virtual transportation to a past setting that is remembered. Third, since VR always offers at least some form of interactivity, narrative VR permits the immersant to have agency within the remembered narrative. These three aspects combine to provide a qualitatively richer experience of memories in VR than was possible in conventional narrative media. We show how creators have leveraged these specific affordances of VR in support of narrating memories by analyzing two case-studies. First, we look at an example of traumatic collective memory in The Book of Distance (2020), which narrates a family history of forced removal and internment of Japanese immigrants in Canada during World War II from the perspective of the victim’s grandson. Second, we discuss Is Anna Ok? (2018), which lets the immersant experience two sisters' memories of traumatic brain injury, once from the perspective of the injured victim and once from the sister who witnessed the accident.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".