Onscreen and off-screen flesh and blood: performance, affect and ethics in Catherine Breillat’s films
Bibliographic record
Abstract
In this article, I argue that actors’ and actresses’ performances are key objects of analysis in addressing the affective and ethical challenges of extreme films. Breillat’s Fat Girl (2001) and its fictionalized making-of, Sex is Comedy (2002), incites an ethical engagement not merely in the sense of textual analysis, but requires a deeper investigation of the star, Roxane Mesquida. The reflexivity of the paired films – the latter as a staged re-enactment of the sex scene of the former, re-performed by lead actress Mesquida – results in an experience of an affective bleed: once we see the performative challenges Mesquida faces in the latter film, we return to the earlier and are doubly affected by both the horror of fictional rape, and the trauma the actress underwent to convincingly perform that violation. These two films pose the question of whether onscreen acts of physical and emotional violence manifest in the bodies of actors and actresses off-screen, and further, to what affective and ethical end. In agreement with Kath Dooley (2014, ‘“When You Have Your Back to the Wall, Everything Becomes Easy”: Performance and Direction in the Films of Catherine Breillat.’ Studies in French Cinema, 14: 2, 108–118), I claim that Breillat must place these demands on her performers in order for her critiques of patriarchy to gain their strength.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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".