Political Engagement in Carne y Arena by Alejandro González Iñárritu
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it