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Introduction

2020· reference-entry· en· W4242533417 on OpenAlexaff
Kirsten Yri, Stephen C. Meyer

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldSocial Sciences
TopicHistorical Influence and Diplomacy
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMedievalismMiddle AgesRomanticismIdeologyNarrativeScholarshipHistoryLiteraturePeriod (music)ArtClassicsArt historyAestheticsAncient history

Abstract

fetched live from OpenAlex

Medievalism—broadly construed as retrospective immersion in the images, sounds, narratives, and ideologies of the European Middle Ages—has left a powerful mark in both art music and popular music culture of the nineteenth and twentieth centuries. Picking up with the extent to which the distinction between medievalism and medieval studies is possible and/or desirable, the introduction acknowledges that medievalism involves multiple discourses of the Middle Ages, and even when its goal is recovery, it is embroiled as much in contextual modes as in scientific methods. As the chapter discusses, questions about authenticity and intentionality central to much of the scholarship on music histories of the Middle Ages, performance practices of medieval music, and medieval music revivals are valid, but they are called on to speak in a complex sociohistorical and aesthetic analysis. The introduction also provides an overview of several taxonomies that inform the theorization of medievalism as well as an overview of the six sections of the handbook and some of the themes (nationalism, romanticism, rejections of modernism) that connect them.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.539
Threshold uncertainty score0.769

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0070.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4610.298

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 head0.047
GPT teacher head0.341
Teacher spread0.294 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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