Confused Robots and Incompetent Humans in Jean-Pierre Jeunet’s Bigbug (2022)
Bibliographic record
Abstract
Jean-Pierre Jeunet has been working on the presentation of struggling human bodies, in his films such as Delicatessen (1991), La Cité des enfants perdus/The City of Lost Children (1995), and most recently, Bigbug (2022). In Bigbug’s fictional 2045, human beings are finally liberated from the duties of house chores. Nevertheless, they no longer have self-determination. Because of the A.I. system’s error, human characters in this film are confined in a house with house robots. This article studies the relationship between human bodies and artificial intelligence in Bigbug, through the lens of transhumanism and animality studies. By transhumanism, I mean an attempt to transform and adapt one’s body with or without technology. I argue that Jeunet’s sense of humour functions as an impetus to encourage his characters to continuously adapt and transform themselves in limited space. Moreover, the animality was discussed differently in this film: instead of comparing animals to humans, Yonyx, the A.I. androids identify humans with animals, mocking the animality of living beings. Referring to Steen Christiansen’s term "terminal films," this article examines how immobile, restricted human bodies co-exist with and resist artificial intelligence in our everyday household.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".