Idolatry and the Civil Covenant of Photography: On the Practice of Exhibiting Images of Suffering, Degradation, and Death
Bibliographic record
Abstract
Abstract Exhibiting perpetrator photographs of suffering and death presents a series of curatorial problems for museums and galleries. Unlike photojournalist images taken to inform a social conscience, the initial creation and circulation of such photographs have historically been implicated in the violence they depict. Beyond skepticism as to photography’s capacity to arouse a moral impulse, exhibitions of perpetrator photographs have been criticized for promoting voyeurism and extending suffering through the reiteration of images of human degradation. I consider how a problem central to Jewish theology might speak to such curatorial concerns, specifically the question of what constitutes the practice of idolatry. In this context I explore issues related to the ethics of visuality, developing the implications of Leora Batnitzky’s reading of Franz Rosenzweig’s cultural writings for my own concerns regarding the museological practice of public history.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.077 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".