Autodestruction in Lebanon: The Testimonial Uncanny and the Birth of Knowledge in Walid Raad’s <i>The Atlas Group</i>
Bibliographic record
Abstract
This paper is concerned with the methodological question of what it means to produce “cultural politics from below,” in opposition to “cultural politics from above.” This approach emerges from an examination of memory and memorialization in the context of traumatic events, in particular, the Lebanese civil wars, which took place from 1975–91. My discussion focuses on debates that took place regarding the restoration and rehabilitation of Beirut’s war-torn city-center. I examine how the nation-state sets out to memorialize its own place in the historical record in comparison to how artistic practices question memorialization by interrogating the historical record and the archive’s role in creating memory. I begin this essay by thinking about “explosions” and how the motif of the explosion occupies a particular place in work on testimony and trauma. Often explosions cause trauma, but within the critical literature on testimony, the idea of the explosion has taken on creative, productive elements, becoming a way to conceptualize the birth of testimonial knowledge. In Walid Raad’s project, The Atlas Group, I argue that he represents the possibilities and limitations of re-making testimonial knowledge through his creation of a visual inventory of news-media photographs in the aftermath of car bombings.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".