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Record W2593737902 · doi:10.3138/seminar.53.1.02

Die Toten im dritten Raum: Grabmäler als Orte der Begegnung zwischen Angehörigen verschiedener Religionen bei Wolfram von Eschenbach und Wirnt von Grafenberg

2017· article· en· W2593737902 on OpenAlexvenueno aff
Astrid Lembke

Bibliographic record

VenueSeminar A Journal of Germanic Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical, Literary, and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeOrder (exchange)NegotiationHumanitiesPhilosophyArtHistorySociologyLiteratureSocial science

Abstract

fetched live from OpenAlex

This article explores the question as to how and when tombs in medieval narrative texts can be viewed as ‘third spaces’ that are used in order to define relations between members of different religious communities. In Wolfram von Eschenbach’s ‘Parzival’ and ‘Willehalm‘ as well as in Wirnt von Grafenberg’s ‘Wigalois’ tombs and the spaces around these tombs are presented as places where Christians and ‘Heathens’ negotiate their coexistence. In ‘Parzival’, for example, Gahmuret's tomb is a memorial to a shared past that does not cause any future cooperations. In ‘Willehalm’ the burials of members of both groups demonstrate the present need to strictly distinguish between the religious spheres. In ‘Wigalois’, however, a ‘heathen’ princess’s tomb becomes a place in which new alliances can be formed. It can thus be considered a ‘third space’ – if only temporarily. Moreover, it illustrates how a narrative text about the encounter between religious and worldly semiotic systems integrates Christian imperatives but also uses them in order to create new norms of courtly existence.

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)0.001
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 score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.012
Scholarly communication0.0060.007
Open science0.0000.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.059
GPT teacher head0.314
Teacher spread0.254 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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