Bibliographic record
Abstract
The documentary concept is a 'notoriously slippery eel,' or even the slipperiest one in the history of cinema (Kahana, 2016). With the involvement of virtual reality in the production of documentaries, the dialectic between virtuality and authenticity makes this concept even more elusive. Does documentary exist in VR films? When the spaces and mise-en-scène of the documentary are all artificially created through computing software, can it still be considered an authentic form of a documentary? How can this sub-genre of documentary continue to exist as a kind of proclaimed ‘non-fiction’ when the film is based entirely on fictional visual input? This paper aims to put the discussion on visual realities aside and provides a perspective for understanding how real auditory characteristics are built up in virtual environments (VE) of virtual reality documentaries. We develop this argument in three parts. In the first part, we define a virtual reality documentary and distinguish it from other virtual reality films based on Bill Nichols’s analysis of the boundary between traditional documentary and other types of films. Then, we describe the design and implementation of the aural simulation systems with HRTF (Head-Related Transfer Function) in virtual reality documentary sound design to illustrate how it could reproduce realistic physical hearing for viewers. In the final part, we explore the concept of ‘soundscape’ as it applies to VR documentaries, attempting to show that the sense of authenticity in the audition is related to generating the genius loci (spirit of place) of the viewers based on the case analysis of Anne Frank House VR. Such a spirit of place is embedded in the hearing experience of those who experienced it.
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 | 0.008 |
| 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 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".