Bibliographic record
Abstract
When critics admonish their opponents for circulating mere conspiracy theories, they are disparaging them for subscribing to facile accounts of socio-historical phenomena that are more sophisticated and aleatory than such heavy-handed narratives apprehend. Unfortunately, this kind of disavowal has the unfortunate side-effect of precluding conspiracy theories from more serious philosophical consideration. Arguably the most notorious information age conspiracy theory of the moment is QAnon, a byzantine, messianic truther echo-system that has recently irrupted into mainstream public consciousness. QAnon derives its name from “Q,” a lurid, anonymous, putatively omniscient insider who has been dropping missives on message boards about Donald Trump’s clandestine war with a satanic, sex-trafficking, election-fixing cabal that lurks beneath the liberal establishment. In order to engage with QAnon as a cultural phenomenon, my article probes the rhetorical coordinates of the popular concept of conspiracy theory through optics provided by Kenneth Burke and Jodi Dean. Drawing on the recent media scholarship of Carrie Rentschler, Kate Starbird, and John Durham Peters, I then examine QAnon culture as a misguided activist modality of witnessing (what Alain Badiou might call a “pseudo-Event”) precipitated, in no small part, by rhetorical and algorithmic architecture that subtends an ever-increasing proportion of human subjectivity. I conclude with some reflections on the viability of what media theorist Jonathan Sterne terms an "intervention."
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".