Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.055 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".