“Imprisoned Photographs”: The Looted Archive of Photo Rissas (Rassas)—Ibrahim and Chalil (Khalil) Rissas1
Bibliographic record
Abstract
This essay is the first dedicated solely to the work and archive of Ibrahim and Chalil (Khalil) Rissas (Rassas). Ibrahim was one of the pioneers of Palestinian photography in Jerusalem in the early twentieth century and Chalil, his son, was one of the first Palestinian photojournalists in the 1940s. Rissas’ images were looted and seized by Israeli officer from the photographers’ studio, from the body of a soldier or “slain Arab,” or “rescued” from a burning shop. Those photographs that had been looted from the studio, were the first collection I found in the Israeli military archives. In this essay I chart and analyze the way Rissas’ images were looted on several occasions and the moral, sociological, and political consequences of these acts—for instance how the looted object becomes a symbol of triumph or acts as a vehicle to dehumanize the enemy. The essay is also the first to focus on the phenomenon of pillage by individuals who transfer the looted cultural assets to colonial official archives where they are ruled by the colonial administration. It thus reflects not only the responsibility of states in the process of “knowledge production” and on their role in distorting the past and rewriting history by various bureaucratic, linguistic, and legal means, but also on the role of citizens in these destructive processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".