Abu Ghraib and the commemorative violence of war trophy photography
Bibliographic record
Abstract
The photographs from the Abu Ghraib scandal are horrific, but they are also understandable. Simply put, the Abu Ghraib photos are purposeful compositions that highlight victory over the enemy Other in war. The photos illustrate sexual and racial violence, founded upon postcolonial narratives, but this is only a starting point for their significance. I address how meaning is made for the U.S. military personnel who took photographs of naked Iraqi detainees at Abu Ghraib, by looking backward to soldiers’ photography from WWI and II, and by considering soldiers’ online sharing of photographs in the present, examining roughly fifty photos total. The relationships between photographic materiality, emotional and gestural communication, and the production of cultural memory, disseminated via networked circulation, all shape how soldiers’ wartime photographs come to be regarded. Employing an interdisciplinary approach, this research draws upon war photography; visual culture and communication; sociology of groups and ritual; sociology of emotion; combat histories; memory studies; and online photo sharing practices. In so considering, the Abu Ghraib photos are not unique, and are instead grouped within the greater concept of the “war trophy.” I expand on this concept by defining “war trophy photography” as the entwined practices of war photography and trophy collection, rooted in ritual and group solidification. Staged to depict the violent conquering of the enemy, I argue that war trophy photography recognizes war efforts through the construction of a visual record, one that reproduces relations of dominance and submission. I call this representation “commemorative violence,” a central concept I develop to define the war trophy photograph. In addition to grounding the Abu Ghraib photos historically, I review their visual semiotic, cultural significance, such as with the “Doing a Lynndie” meme, which features civilians gesturing in thumbs-up toward a downtrodden individual, copying the same gesture as often used in the images from Abu Ghraib, and the now defunct site “Now That’s Fucked Up,” which briefly allowed soldiers in 2005 to trade gruesome war trophy pictures for pornography. The conclusion reflects on war trophy photography with the topical consideration of drones, ultimately suggesting that drone warfare photos are expressionless because of the overt absence of people.
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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 | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".