Virtual Humanity: Empathy, Embodiment and Disorientation in Humanitarian VR Experience Design
Bibliographic record
Abstract
With 360-degree filmmaking and Virtual Reality (VR) – the audience can now be immersed in the milieu of the filmed location. Hitherto hard to reach territories, and hard to portray narratives can now be realised and experienced first hand – rendering new opportunities for empathetic, political and cultural engagement. The possibilities of these new technologies of capturing and exhibiting locations and situations have drawn journalists, activists and documentary makers to the form. In this article, we examine four such case studies that have sought to make use of the VR cinema 360-degree format to illuminate specific aspects of human experience. In the award winning Notes on Blindness: Into Darkness the filmmakers and VR designers create an emotionally powerful experience based around visual impairment. In the second case study, Home: Aamir, theatre practitioners (National Theatre) create an experience that positions the viewer in an immersive first hand account of one migrant’s journey from the Sudan to the Calais Jungle camp. 6 x 9 was produced by Guardian journalists and places the viewer into a harrowingly realistic and challenging experience of a US solitary confinement cell. The final example, Draw Me Close, (National Theatre and the National Film Board of Canada) is a complex and experimental piece of virtual theatre that examines grief, loss and bereavement. To describe the close engagement required for the study of these four examples we propose a ‘virtual-reality ethnography’ methodology and evolve an initial framework of attention through which to engage with and research the emergent complex experiences being conceived and delivered through VR and 360-degree film-making and experience design.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".