The politics of torture in antagonistic politics, and its displacement by the regime of the arts: Abu Ghraib, Colombian paramilitaries and Fernando Botero
Bibliographic record
Abstract
This article proposes a functional analysis of torture, following Luhmann’s social systems theory. Its guiding hypothesis is that torture belongs to a specific type of politics, namely antagonistic politics, and that violence is an essential part of this particular presentation of politics. The article goes on to propose a view on the praxis of torture, which is observed by making a case based on two seemingly isolated situations: the first, a systematic practice in the context of the Colombian conflict: torture followed by dismembering of identified enemies. The second one is torture of presumed terrorists at Abu Ghraib, in the context of the Iraq war. The analysis identifies the common aesthetic elements in the technology of torture in order to ascertain their function within political communication. The analysis finishes by examining the response to torture from an artist’s stance: Fernando Botero’s paintings of both the Colombian violence and the torture in Abu Ghraib. This association introduces the observation of the uses of torture within artistic communication, in contrast with former uses within antagonistic politics. The article concludes by situating the relation between politics and aesthetics, and the conditions for their mutual dialogical interference: from politics towards aesthetics, and from aesthetics towards politics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".