Bridging the Cognitive/Collective and Supply/Demand Divides in Conspiracy Theory Research
Bibliographic record
Abstract
ABSTRACT Objectives Despite the fast growth of the social scientific literature on conspiracy theories, fragmentation rather than dialogue is the norm across disciplines. One such division is between the individual/cognitive versus sociopolitical dimensions of conspiracy beliefs, which are often studied in isolation. This article aims to contribute to bridging the gap. Methods We carry out a selective review of the post‐2010 literature that approaches conspiracy theories from (social) psychological and political sociological perspectives to highlight and compare their main inquiries and findings. Results The examination finds that the psychological scholarship, which deals with individual and group‐based variables, is more attuned to studying the public “demand” for conspiracy theories. By contrast, research on conspiracy theories in collective phenomena such as populism and social movements is more inclined to elucidate the “supply” side of the equation. Conclusions In addition to the quantitative‐qualitative rift already identified in the literature, conspiracy theory scholarship is also shaped by the divides that pertain to the level of analysis and the supply and demand sides of the conspiracy “market” dynamics. The article argues for a closer dialogue between micro (individual), meso (interpersonal), and macro (national/global) levels of analysis to integrate the demand and supply factors nourishing conspiracy narratives.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".