Political Conspiratorial Beliefs are Likely Over-Estimated and Transitory
Bibliographic record
Abstract
Prominent conspiracy beliefs, such as QAnon or 2020 Presidential Election beliefs, constitute a unique form of conspiracy theories that are often explicitly partisan, "politically instrumental conspiracy theories" (PICTs). PICTs can spread rapidly, quickly becoming consensus beliefs among partisan in-groups. But PICTs are not necessarily deeply held, rather primarily serving immediate instrumental partisan needs. We use novel survey list experiments to estimate the prevalence of QAnon and 2020 Presidential Election conspiracy theories in the United States. We find that standard survey techniques likely overestimate the prevalence of PICTs by a factor of $\sim$2. Over-reporting of PICTs is driven by right-wing media consumption (QAnon), and partisanship (2020 Presidential Election). Further, we find that PICT attitudes are heterogeniously related to engagement in political and pro-social behaviors. While the prevalence of PICTs is commonly over-estimated and the beliefs may be transitory, they can serve an instrumental role in the contemporary American electorate.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".