Crises of Charismatic Legitimacy and Violent Behavior in New Religious Movements
Bibliographic record
Abstract
There is a marked tendency for the popular media to emphasize the role of supposedly “manipulative and mad” charismatic leaders in the tragic events surrounding the deaths of members of new religious movements. The focus on the role of the leader, perhaps the only member of the group about whom there is some information, reflects both our age-old desire to personify evil and the dramatic requirements of telling a good story. Clearly, there is some truth to the presumption that the powerful leaders of these groups were instrumental in bringing about the violence. Equally clearly, however, the focus on the greed, lust, or mental instability of the leaders fails to explain adequately why so many people were willingly to place their fates so completely in their hands. Why would the faithful follow these seemingly deranged leaders to their death? For decades, the answer to this question hinged on assuming that the followers were the victims of systematic programs of “brainwashing” or “mind control.” But the empirical evidence acquired by scholars of religion has soundly discredited that assumption, so we must look elsewhere for answers (Dawson 1998: 102–127). The dramatic and exceptional incidents of cult-related violence we have witnessed in the last several decades stem, as Robbins delineates in the previous chapter, from the convergence of many factors, both endogenous and exogenous. The impact of charismatic leaders is one of the constants of these incidents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".