How conspiracy theorists argue: epistemic capital in the QAnon social media sphere
Bibliographic record
Abstract
What is the role of different epistemic modes in how authority is established in right-leaning conspiratorial narratives? This paper sets out to answer this question through a mixed methods analysis. The first section sets out a model for the analysis of epistemic contestations, using six epistemic modes. This is then applied to a data set of Telegram posts in which key terms are used to identify these epistemic modes. Two questions were then asked of the data. First, how is power related to different kinds of knowledge claims in the far-right conspiratorial milieu? Second, what is the role of these different epistemic modes in how authority is established in right-leaning conspiratorial narratives? How does the epistemology of QAnon influence how they argue? We found that while a broader set of epistemic modes could be identified, there were contestations internally also, particularly around moments of “failed prophecy,” and the role of Christianity and esoteric spiritualities.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".