MétaCan
Menu
Back to cohort
Record W7116678368 · doi:10.3390/rel17010010

“Making” Testimonies: Charismatic Phenomena and Speech Practice in the True Jesus Church of a Southern Fujian County

2025· article· en· W7116678368 on OpenAlexfundno aff
Zhehong Hong

Bibliographic record

VenueReligions · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMedia, Religion, Digital Communication
Canadian institutionsnot available
FundersPeking UniversityUniversity of TorontoNational Chengchi University
KeywordsCharismaEthnographyNarrativeConstruct (python library)TRACE (psycholinguistics)Rhetorical questionPower (physics)Identity (music)

Abstract

fetched live from OpenAlex

Moving beyond the debate on cultural continuity, this article investigates the micro-mechanisms by which charismatic experiences are produced and authenticated in a True Jesus Church (TJC) community in Southern Fujian. Based on ethnographic fieldwork conducted between 2022 and 2024, the study proposes the concept of “making testimonies” to trace the social production of charisma. The analysis identifies three consecutive stages in this mechanism: (1) in everyday interaction, pastoral rhetorical prompting anchors believers’ scattered sensory experiences to church-recognized experiential types; (2) in ritual settings, complex life histories are disciplined into standardized narratives of “grace and conversion” to align with communal identity; and (3) through mediatization, oral accounts are verified, edited, and fixed into an authoritative archive of collective memory. The study argues that these practices are not expressions of “indigenization” but are strategically employed to construct an authenticity that validates the TJC’s theological claim as the “exclusive church of salvation.” By revealing how modern organizational power and media technologies configure the local “landscape of the Spirit,” this research offers a dynamic, practice-oriented framework for understanding Chinese Christianity.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.300
Teacher spread0.258 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueReligionsSame topicMedia, Religion, Digital CommunicationFrench-language works237,207