Bibliographic record
Abstract
The chapter will center on a photograph by contemporary Canadian artist Jonathan Hobin entitled A Boo Grave, relating it to some of the infamous Abu Ghraib photographs taken by the U.S. Military Police during the Iraq War, by focusing on the representation of the child. It will discuss in what ways it has been possible for the ‘War on Terror’ to infiltrate children’s bedrooms and the implications of bringing the gruesomeness of war into the ‘play area’. The chapter will examine both the child as a horror object, and the significant role of the media and parents in the construction of the child’s image. The perviousness of the children to world events and the perversion of the child’s image will then be major topics discussed through Hobin’s lens. The analysis will be further developed, as I will examine signs of dubious ‘playfulness’ (associated with role-playing) within the ‘Abu Ghraib’ photographs themselves, especially the ones where the members of the Military Police appear smiling or giving the viewer the ‘thumbs up’ while constructing pyramids using detainee’s naked bodies, forcing a leash on one of them and ‘dressing up’ one other with a hood and cloth arranging him much like a child would do with a doll. By dehumanising the detainees in such a way, those photographs could be read as a sort of performance. This ‘staging’ will therefore be compared with Hobin’s photograph.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".