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Record W2039769408 · doi:10.7202/1028464ar

« Faire communauté ». Comment les Églises fabriquent leurs convertis ?

2015· article· fr· W2039769408 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThéologiques · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article vise à explorer et à questionner le fonctionnent d’une institution religieuse dans sa capacité à faire communauté, c’est-à-dire à former et à façonner des croyants. Ce travail s’appuie sur des recherches menées lors de mon doctorat, qui présentait une analyse comparative des modalités de la conversion dans les trois religions monothéistes dans la France contemporaine : judaïsme, catholicisme et islam. L’intérêt d’une telle comparaison permet de qualifier le travail des « institutions religieuses » (au sens large) face aux « nouveaux convertis ». En référence aux travaux de J. Lagroye, nous verrons quelle est la réalité de ce travail, en supposant qu’une institution doit « faire communauté ». Pour cela, elle doit imposer une vision générale de l’adhésion et de l’appartenance, en mettant à distance les émotions des convertis et en imposant du banal. Nous pourrons alors conclure sur la tension intrinsèque des institutions religieuses, face à la question de la conversion et l’impératif de « faire communauté » : utiliser les émotions pour mieux les faire oublier.

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score1.000

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 studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.376
GPT teacher head0.428
Teacher spread0.052 · 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