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Record W4392210703 · doi:10.1177/09719458231225863

The Subject Behind the Object: The Language of Things in the Time of the Crusades

2023· article· en· W4392210703 on OpenAlexfundno aff
Anne E. Lester

Bibliographic record

VenueThe Medieval History Journal · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
FundersUniversity of TorontoUniversiteit van AmsterdamUniversity of CambridgeUniversity of PennsylvaniaSmithsonian Institution
KeywordsMateriality (auditing)MaterialismAestheticsObject (grammar)Relation (database)EpistemologySociologyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

This paper takes as a starting point the slippage between bodies and things as an idea and moment to interrogate an epistemology accessible through materiality. I explore the methodology of materiality, suggesting that one of the major contributions of material studies is a renewed attention to the dynamics of materialism embedded in the objects themselves: those unnamed subjects who lie behind or just beyond an object’s presence, who played a role in its making, its movement and its meaning. To this end, the paper takes shape around a series of case studies of bodies and/as things drawn from the crusader world described by Jean de Joinville, namely stones, cloth and captives (bodies made into things). Attendant with these objects were deeper theological and ontological questions about the role of matter in relation to faith and the divine. The period of the crusades was a particularly revealing moment for the tensions and beliefs surrounding the work of material religion. By looking at these case studies I hope to bridge the divide between intellectual and theological concerns with materiality and the material presence of human labour in things and thereby to think sociologically with materiality. This is an invitation to take up the imperative behind material studies to go beyond words and see the subjects behind the objects.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.218
Teacher spread0.196 · 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.

Study designQualitative
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
Published2023
Admission routes1
Has abstractyes

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