Bibliographic record
Abstract
Humanitarian organizations, journalists, and artists are increasingly turning to virtual reality (VR) and immersive filmmaking because of its ostensibly unprecedented ability to conjure empathic feelings that lead to humanitarian action. Recent media studies scholarship attends to the possibilities and pitfalls of curating empathy through VR in the context of documentary filmmaking; however, these analyses primarily focus on VR’s unique visual address. The status of the participant’s body, as it exists in the physical world and as it is conjured within the virtual environment, remains under-explored in scholarship on immersive media and humanitarianism. In this paper, we offer a comparative analysis of embodiment in two recent multisensory VR film installations with humanitarian themes: Alejandro González Iñárritu’s Carne y Arena (2017) which stages an attempted border crossing between Mexico and the United States; and Hero (iNKStories, 2018), which places participants into an unnamed Syrian village during an air raid. Using bodily absence as a framework, we argue that agency, responsibility, and a humanitarian subjectivity are ambiguously constructed through the sensing of bodily and psychic borders within these contemporary VR installations. We conclude that humanitarian VR is better understood as a technology of encounter rather than one of empathy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".