Bibliographic record
Abstract
Initially formulated in the 1970s when large numbers of former counterculturists were joining alternative religions, the youth-crisis model of conversion posited that new recruits were predominantly young people whose involvement could be explained as a function of their youth (e.g., as an adolescent developmental crisis). The present study presents statistics on recruits to seven different contemporary new religions that fundamentally challenge this item of conventional wisdom. Six out of seven data sets also embody a striking pattern of gradually increasing age across time for new converts. In addition to uncovering the growing age-at-recruitment pattern — which I designate the E-correlation — I argue that: (1) With the exception of efforts to understand true youth movements such as Internet Satanism, attempts to interpret conversions to contemporary emergent religions as being a function of the imputed youthfulness of recruits is no longer in touch with the reality on the ground. (2) The persistence of the characterization of converts as youthful reflects a failure to build a strong empirical base for such generalizations. Instead, we have relied upon quantitative work carried out over a quarter of a century ago for much of what passes as conventional wisdom in the study of recruitment to alternative religions.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".