Bibliographic record
Abstract
Abstract This essay, excerpted with permission from my recent book Epidemic Empire (University of Chicago Press, 2021) is about the colonial Gothic and the appearance therein of the paradigm of contagion as a metonym for political violence. I show how Bram Stoker’s Dracula, the most enduring and popular novel in this archive, is so capacious in its allegorical accommodations that it has been read as incorporating nearly every threat and monster of its own time and place, as well as those of its scenes of critical reception. More than indexing historical referents, I argue, the novel is a kind of allegory of allegories, producing new epistemes about multisystemic threats and carrying them forward into the crises of our own moment – global pandemic, terrorism, the collapse of empires. Following Fanon, who invokes the rich mythology of the vampire to point out colonial discourse’s specific habits of dehumanization, we can locate Dracula in an important archive of colonial writing that sets the ‘science’ of monsters on its infinite course and advances a reading practice that borrows from the surveillance apparatus and epidemic narrative strategies of colonial disease literature. Because of its extraordinary durability, Dracula is a crucial textual conduit in the transmission of minor ideas about the circulation of terror, rebellion, racialized bodies, foreign materials, and communicable disease from largely forgotten nineteenth-century texts and contexts into the common sense of late empire and late capitalism. Although Dracula is not a terribly self-conscious, systematically researched, or ideologically consistent text, it is decisive in its effects.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".