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Record W2945226018 · doi:10.29173/cons29366

They Tore God Limb From Limb

2019· article· en· W2945226018 on OpenAlexaffvenue
Katlyn Kichko

Bibliographic record

VenueConstellations · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicReformation and Early Modern Christianity
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNarrativePossession (linguistics)NormativeInstitutionSociologyScope (computer science)Face (sociological concept)Order (exchange)MillerEpistemologyAestheticsHistoryLawPhilosophyLiteraturePolitical scienceSocial scienceArt

Abstract

fetched live from OpenAlex

This paper interacts specifically with two separate texts, that is Michel de Certeau’s The Possession at Loudun and Carlo Ginzburg’s The Cheese and the Worms: The Cosmos of a Sixteenth Century Miller. Both of these texts present a narrative of religious turmoil, demonic possessions and a heretical Inquisition, respectively, and the events which surround a single religious dissenter. Examining the two heretical men presented within these texts in comparison allows for an understanding of Catholic Church dogma during the age of the Counter Reformation, and how such an institution managed threats, both external and internal. Moreover, this paper also examines the methodologies behind the historical discourse, in order to understand the validity of the narratives presented, and the scope of historical depth sought. Addressing methodology is crucial when one narrows focus to two singular case studies by two separate historians. Thus, this paper intends to illustrate the threats to normative religious discourse which Urbain Grandier and Menocchio possessed in the face of the Catholic Church, while also demonstrating the methodologies by which the two men are presented within their respective histories.

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: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.010
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.027
GPT teacher head0.210
Teacher spread0.183 · 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
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
Published2019
Admission routes2
Has abstractyes

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