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Record W2323740551 · doi:10.1177/1750635216636136

Beyond Abu Ghraib: War trophy photography and commemorative violence

2016· article· en· W2323740551 on OpenAlexaff
Joey Brooke Jakob

Bibliographic record

VenueMedia War & Conflict · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsTrophyPhotographyPhotojournalismVisual artsAdversaryRepresentation (politics)PremiseHistoryMedia studiesSociologyArtLawArchaeologyPolitical sciencePoliticsComputer science

Abstract

fetched live from OpenAlex

The commemoration of wartime often has emerged alongside brutal practices waged on the enemy, and the photographed events at Abu Ghraib are no exception. Indeed, the composition of these images builds upon a visual history in which certain dynamics are represented within more general and often innocuous combat photography. This article focuses on two things in order to articulate this premise. The first is to outline how ‘war trophy photography’ is the result of the entwined practices of war photography and trophy collection. Mapped using a combined comparative historical approach and visual semiotics, this research draws upon three images, one from WWI, another from WWII, and one from Abu Ghraib. Specifically to highlight how posing within these photos acknowledges the images as trophies, the second function of this article emerges with the concept of ‘commemorative violence’, as the representation is fused with emotional communication and cultural memory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.020
Scholarly communication0.0080.005
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.025
GPT teacher head0.240
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2016
Admission routes1
Has abstractyes

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