Narrative Skin Repair: Bearing Witness to Representations of Self-Harm
Bibliographic record
Abstract
A on feminism and popular culture, I attended a presentation on Marina de Van's () fi lm Dans Ma Peau (In My Skin).¹From the outset, the presenter cautioned us that the fi lm contained graphic imagery of self-harm including the protagonist tearing at, sucking on, and eating her own, self-infl icted fl esh wounds.She then proceeded to show a few clips from scenes she described as "relatively inexplicit" compared to the rest.Upon the fi rst, three members of the already small audience sprang out of their seats and hurriedly left the room.Exactly what did these three not want to see, think about, or perhaps feel such that they were compelled to leave this way?Put diff erently, what did the invitation to bear witness to representations of self-harm evoke that was so unbearable?What, on the other hand, motivated the rest of us to stay?Moreover, did the feminist context matter here?And does this instance say anything
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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 | 0.003 | 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 | 0.016 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".