Sympathy for the Devil: (Re)Reading The Satanic Verses after 9/11 and Learning to Love the Monster (Within)
Bibliographic record
Abstract
This essay seeks to understand the extent to which Salman Rushdie's The Satanic Verses troubles dominant constructions of the grotesque in order to comprehend whether and how much we can be “beckoned” or “interpellated” by lives that are different from our own. Rushdie’s novel and, in particular, the character of Saladin Chamcha, offer a challenge to the rhetorical binaries of anti-terrorism discourse that seek to divide the world between innocent victims and terrorist others. (Re)reading The Satanic Verses in the context of the War on Terror destabilizes dominant constructions of monstrous otherness by appropriating instances of the grotesque. This essay will provide a close reading of the parallels between Chamcha’s “descent” into monstrosity and the anti-terrorist discourse of contemporary Britain to consider instances of monstrosity and the grotesque as openings to a new way of apprehending others in a post 9/11 world
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".