The “New Collective Technoid Body”: Immersive Environments and the Im/mobile Refugee Subject in Gabo Arora and Chris Milk’s Virtual Reality Film Clouds Over Sidra (2015)
Bibliographic record
Abstract
Clouds Over Sidra (Arora and Milk, dir. 2015) is a virtual reality documentary film that narrates the forced displacement and subsequent resettlement of the protagonist Sidra and her family in the Za’atari refugee camp. This article first sheds light on the participatory premise of the VR machine and how it impacts the spectators’/participants’ assessment of the narrative experience. Second, it argues that rendering the im/mobility of the Za’atari refugee camp dwellers, the film falls prey to the arbitrary nature of the forced displacement and the complex character of the transient place of the refugee camp. While VR is an experiential medium that offers a deep sensory experience, the English voice-over, which attempts a ‘linear’ recounting of Sidra’s story, offsets the film’s “shooting in every direction” (Milk, 2015). Lastly, moving beyond the film’s structural failure, the article looks at the infinite versions the VR film offers its remote navigators (and on-site protagonist) and how they might serve as a metaphor for the intersection of histories in the refugee camp. Ultimately, this article seeks to reach a new understanding of the forcibly im/movable subject in the context of the refugee camp.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".