Thinking for Themselves: Bootstraps Discourse and the Imagined Epistemology of Reactionary YouTube Audiences
Bibliographic record
Abstract
In recent years, popular interest in disinformation has coalesced around a series of high-profile events, starting with the Brexit referendum and the election of Donald Trump in 2016. While Facebook and Twitter drew the most scrutiny in the immediate aftermath of these events, attention has turned in recent years to YouTube as a source of right-wing disinformation and radicalization. While the bulk of the extant literature on this topic has focused on the supply of right-wing content on YouTube – including quantitative studies examining the impact of the recommendation algorithm and qualitative studies exploring the rhetoric and micro-celebrity practices of reactionary channels – few studies have examined what draws viewers to the videos they watch. This paper aims to fill this gap in research by analyzing interviews with 18 current and former fans of US-centric reactionary YouTube channels. Based on these interviews, I introduce the concept of bootstraps epistemology as a way of understanding right-wing approaches to accessing political truth and knowledge.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.014 | 0.068 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".