Bibliographic record
Abstract
While the assertion, ‘no one really wants to talk about COVID anymore’, has become a common refrain, cultural evidence suggests otherwise. Rather, cultural materials indicate not only a sustained interest in epidemic and pandemic experiences in the past but also continuing interest in our own pandemic era. However, this interest is often registered through gestures and brief mentions rather than explicit and sustained plague narratives. This paper considers these trends, especially in Gothic works, a literary tradition rooted in hyperbolic representations of threats that also represents disease on frank terms consistent with current medical knowledge. Pandemics appear in Gothic writing two centuries ago through brief references that suggest the daily experience of danger. Pandemic-era television is following the same strategies. Like ‘fevers’ and ‘plagues’ in the early 1800s, COVID-19 can be raised briefly and often indirectly. There is also attention to other aspects of the pandemic, including isolation and misinformation. In the popular Gothic series, Interview with the Vampire (2022–), ‘plague’ and misinformation are captured on terms drawn from earlier Gothic writing and intertwined to reflect on the misinformation of the COVID-19 era.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".