Terrorism Before the Letter: Benito Cereno and the 9/11 Commission Report
Bibliographic record
Abstract
This essay argues that Herman Melville's 1855 novella, Benito Cereno , serves as a historical model for thinking through the construction of paranoid narrative in the 9/11 Commission Report . Melville's text invites a consideration of slavery's role in the preservation of status quo politics, and reveals the means by which the disclosure of secrecy becomes a condition for the legal fiction of slavery to persist. In purporting to reveal the hidden plot of contemporary anti-US terrorism, the US government's 9/11 Commission Report similarly manufactures an acceptable political fiction that compensates for a still deeper failure to promote democratic structures of feeling in response to national trauma. Le présent essai fait valoir que la nouvelle Benito Cereno d'Herman Melville, publiée en 1855, propose un modèle historique qui permet d'examiner en détail le récit parano du rapport de la Commission 9/11 (9/11 Commission Report). Le texte de Melville incite à examiner le rôle de l'esclavage dans la politique du statu quo, et révèle les moyens par lesquels la divulgation de ce qui est secret devient une condition qui permet à la fiction juridique de l'esclavage de persister. En prétendant révéler le complot caché du terrorisme antiaméricain contemporain, le rapport de la Commission 9/11 fabrique de la même façon une fiction politique acceptable qui vient compenser un échec encore plus profond afin de promouvoir les structures démocratiques du sentiment en réponse au traumatisme national.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".