Surviving Armageddon (aka COVID-19) through "Good Omens: Lockdown" fan fiction
Bibliographic record
Abstract
In May 2020, Neil Gaiman and some of the team behind Good Omens (2019–2025) created a YouTube video titled "Good Omens: Lockdown" ("Lockdown"). In this video, the demon Crowley (David Tennant) and the angel Aziraphale (Michael Sheen) have a phone conversation discussing the pandemic and the importance of following the current UK health guidance. "Lockdown" inspired a large amount of fan fiction. I chose a sample of sixteen of the most popular stories in order to examine how fans transformed aspects of the video in ways that may give insight into and comment on potential fissures in the messaging. The analysis revealed that many fan writers actively resisted the normative, prescriptive health messaging of "Lockdown" and instead worked to make the messaging personal and inclusive of complex situational intersections, such as queer identity and mental health struggles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".