Understanding and Addressing the Rise of Conspiracy Theories in the 21st Century—An Introduction to the Special Issue
Bibliographic record
Abstract
ABSTRACT The contemporary social and political landscape is increasingly shaped by a pervasive and often worrisome phenomenon: the proliferation of conspiracy theories. Once relegated to the extremes of public discourse, these narratives now draw significant attention, traversing geographical, cultural, and ideological divides with alarming speed. This special issue of Social Science Quarterly convenes a collection of cutting‐edge research dedicated to dissecting this complex challenge. This introduction provides an overview of the contributions in this special issue and situates them within the broader literature on conspiracy theories. By fostering a multidisciplinary dialogue and rigorous empirical work, this special issue aims to significantly advance our comprehension of one of the most pressing social issues of our time and to equip scholars, policymakers, and the public with knowledge to navigate this unique conspiratorial era.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".