Collections, Collectors, and a Breviary Leaf at McGill (Ms. Medieval 0239)
Bibliographic record
Abstract
Abstract The library of McGill University holds some 250 medieval items, of which roughly fifty are chant manuscripts. These items, which include several complete or partial volumes and numerous fragments, are the result of over a century of collections and donations that have brought manuscripts from elsewhere to Canada. These chant manuscripts have recently been the subject of a multi-year collaborative project involving their high-quality digitization, reviews of their descriptions, and inventorying their liturgical contents on the Cantus Database, allowing greater access to these sources and exploration of their contents and history. After a survey of the McGill collection, I consider the history of the library’s latest chant acquisition, Ms. Medieval 239: a single leaf from a French noted breviary. Digital images, descriptions, physical characteristics, and other metadata have helped to find other leaves of this breviary, now dispersed from Durham to Ottawa, and to localize its chant tradition. Individual pages across various institutions have helped not only to partially reconstruct a now fragmented chant volume, but also to consider the steps by which such leaves came to rest at McGill and other institutions distant from its point of creation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.006 |
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".