Fastest-Growing Religion? Reflections on Contemporary Paganism’s Rapid Growth and How Scholars Describe That Which They Study
Bibliographic record
Abstract
Starting around the early 2000s, many scholars declared that contemporary Paganism was the “fastest-growing religion” either worldwide, or in specific locales. This claim was most often based on data from the 2001 American Religious Identification Survey, which found a dramatic rise in Pagans compared to a similar study conducted ten years earlier. Although Paganism most certainly witnessed explosive growth around this time, there are many reasons to question Paganism’s status as the fastest-growing religion, including social factors that shape data collection and the interpretations that scholars applied to the data. This article analyzes the data that Pagan studies scholars used to proclaim Paganism’s growth, and suggests that the claim represents a legitimation tactic. By suggesting that a group is growing quickly, a fairly meagre population is given increased importance. This enhances the perceived significance of both the community in question and any scholars who specialize in studying that community. Although Paganism is not the only religion to assert this claim, and this statement is no longer as prominent as it once was, publications from Pagan studies that make this proclamation offer case studies which demonstrate how scholars manipulate data to legitimize the topics about which they write.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".