The comparative conspiracy research survey (CCRS): a new cross-national dataset for the study of conspiracy beliefs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article introduces the Comparative Conspiracy Research Survey (CCRS) dataset, an individual-level cross-sectional dataset on conspiracy beliefs in eight countries: Australia, Brazil, Canada, Germany, Lebanon, Morocco, South Africa, and the USA. The dataset contains general conspiracy belief scales, as well as country specific data on dominant conspiracy theories. In addition, the questionnaire contains validated scales of social trust, populist attitudes, authoritarianism, self-esteem as well as items measuring political interest, ideology, and socioeconomic class. In this research note, we present the methodology of the survey and provide an example of how researchers can use the dataset. This example tackles the difference in the relationship between conspiracy beliefs and activism intentions across countries. We highlight that activism is related to conspiracy beliefs in consolidated democracies, but not necessarily in developing democracies or more authoritarian regimes. Lastly, we conclude by laying out several possibilities for research using the CCRS dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it