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Record W4251868732 · doi:10.1017/9781108917353

The Emergence and Revival of Charismatic Movements

2021· book· en· W4251868732 on OpenAlexaff
Caitlin Andrews-Lee

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsToronto Metropolitan University
FundersUniversity of Texas at Austin
KeywordsCharismaMovement (music)Political scienceHistoryArtAestheticsLaw

Abstract

fetched live from OpenAlex

Political movements founded by charismatic leaders are often considered ephemeral. Existing literature argues that because they rest on unmediated, emotional attachments between leaders and followers, these movements either fade quickly after their leaders disappear or transform into routinized parties. Yet, charismatic movements around the world have proven surprisingly resilient and have retained their personalistic core. Focusing on Argentine Peronism and Venezuelan Chavismo, this book investigates the nature and trajectory of charismatic movements from the perspectives of both leaders and followers. Using interviews, focus groups, and survey experiments, Caitlin Andrews-Lee reveals that charismatic movements can emerge, survive, and become politically revived by sustaining - not discarding - their personalistic character. Followers' charismatic attachments to the movement founder can develop into an enduring, deeply affective political identity that successors can reactivate under certain conditions by portraying themselves as symbolic reincarnations of the founder. Consequently, charismatic movements can have lasting, deleterious effects on democracy. Also available as Open Access on Cambridge Core.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.004
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.025
GPT teacher head0.261
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations27
Published2021
Admission routes1
Has abstractyes

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