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The Cantus Database and Cantus Index Network

2022· book-chapter· en· W4213374686 on OpenAlexaff
Debra Lacoste

Bibliographic record

VenueOxford University Press eBooks · 2022
Typebook-chapter
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOnline databaseWorld Wide WebIndex (typography)Computer scienceLibrary scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Perhaps the oldest and certainly the most enduring of online, medieval chant databases, the Cantus Database for Latin Ecclesiastical Chant and its sister site Cantus Index: Catalogue of Chant Texts and Melodies have experienced stimulating growth over many years. Developments in software and web technologies, coupled with institutional and government support, have enabled multiple rejuvenations for the Cantus Database, now in its fourth decade. Although the original manuscript inventories continue to be the foundation of the Cantus Database and the principal focus of many online searches, its expanded contents and the interactive nature of the website allow for a variety of uses as well as the collection of new data from worldwide contributors. Through sample textual and melodic searches, description of the resources in the database, and demonstration of the infrastructure that ensures compatibility and interoperability with other chant research websites, the place and impact of the Cantus Database and Cantus Index in fields related to medieval musicology and digital humanities are explored in this chapter. The well-known “Cantus” websites, traversing into public musicology and engaging academic crowdsourcing, continue to supply scholars with both raw data and comparative digital tools for chant research, all freely accessible online as the products of collaborative efforts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.069
GPT teacher head0.185
Teacher spread0.116 · 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 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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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