Bibliographic record
Abstract
Created by Mexican awarded director Alejandro González Iñárritu, Carne y Arena is an immersive mixed-reality installation that allows visitors to experience traumatic and violent incidents with illegal immigrants crossing the Mexican–US border. Carne y Arena’s mixed reality combines VR experience with physical components, turning it into a multisensory, bodily immersive experience. As part of the art installation, the whole VR arena is surrounded by the remains of a wall’s border; while inside, actual immigrants’ clothes and objects are also exhibited. Another component is the documentary aspect, where real-life characters recount their stories through video testimonies. Iñárritu immerses and makes the visitors experience refugees’ stories first-hand while exploring their emotional reactions to traumatic realities through a spiral of corporeal sensations and entertainment spectacle. According to Iñárritu, the intent is to subordinate technology to the human condition. Technology does mean nothing unless it can reveal or denounce people’s situations. Therefore, technology must be subordinated to humans, humanity, and art. “I despise technology,” says the filmmaker. But, has film lost the power to engage the viewers emotionally? Can virtual reality simulate refugees’ dispossession (the sense of the self) and alleviate society’s consciousness? In this paper, I examine the role of a museum installation featuring refugees’ discourses; the VR technology in bringing forward the visitor’s social engagement; and the issues the filmmaker address, such as the refugee’s experience in contemporary global society.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".