A Data-Driven Taxonomy of Metaphysical Belief Systems
Bibliographic record
Abstract
Studies about religious and spiritual believers often ask participants to pick a label from a list that the researcher provides (e.g., christian, nonreligious, atheist). While useful, this imposes rather than searches for the way that people actually cluster together based on their beliefs. In two exploratory US samples (N1 = 917; N2 = 506), we used Latent Profile Analysis, a bottom-up method to cluster participants based on their belief profiles, to identify three distinct types of believers: skeptics (low supernatural and high scientific beliefs), believers (high supernatural and low scientific beliefs), and those who took a middle path (middling supernatural and scientific beliefs). Profiles mapped on well, but not perfectly with self-report categories, and outperformed self-report categories when used to predict other measures of well-being and belief. In the study which is the subject of this preregistration, we will test whether these profiles replicate in a UK sample.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.011 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".