Legacies of Tortured Sensibility; or, what Shakira learned from Sade
Bibliographic record
Abstract
Apropos of the title, this essay traces the surprising connections between the eighteenth-century pornographer and the contemporary Latina superstar’s portrayals of eroticized torture, as well as elucidates the cultural significance of what I am calling a legacy oftortured sensibility. By illuminating how the gendered spectatorial politics of sensibility—particularly in its fetishization of the (female/feminized) body in pain—continues to inform the numerous interlocking discourses of race, gender, and sexuality we have inherited from Sade’s Europe, and especially from the early sentimental novel, this paper demonstrates how the transnational artist taps into a Sadean resistance to figurations of distressed hearts and flayed skin as sites of geopolitical and individual transcendence. Finally, examining120 Days of Sodomand “La Tortura” side by side revitalizes attention to the ethical crisis surrounding aesthetic voyeurism: where does the anguish of reading Sade—with his relentless scenes of corporeal torment—go?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".