Schier, Volker, and Corine Schleif, project dirs. Opening the Geese Book
Bibliographic record
Abstract
Medieval and Renaissance manuscript choirbooks were prestige objects, expensive to produce and highly prized.Containing polyphony or plainchant, they were inscribed on vellum and richly decorated, with illuminated capitals marking out major divisions and special feast days.As large objects, designed to be placed on a lectern and sung from by groups of men and boys, they reflected the wealth and status of the church and city for which they were prepared.The chant book (divided into two volumes and now held in the Morgan Library and Museum in New York) at the heart of this project was copied for the church of St. Lorenz in Nuremberg between 1507 and 1510 and reflects that city's self-fashioning during the period immediately before the Reformation.It contains the plainchant proper and ordinary Mass chants for the liturgical year.Decorations include elaborate acanthus patterns and scenes from the Bible.There are also satirical scenes, including an image of seven geese and a fox singing from just such a choirbook, directed by a wolf; this has led to the combined volumes being dubbed the "Geese Book" (Gänsebuch in German).The impressive Opening the Geese Book website is the result of a research project based at Arizona State University, directed by art historian Corine Schleif and musicologist Volker Schier, and involving a number of other institutions and collaborators.Funding was provided by a variety of sponsors including the National Endowment for the Humanities, the Samuel H. Kress Foundation, the hosting university, the Archdiocese of Bamberg, and a couple of Nuremberg banks.The website was completed in 2012, after 10 years of work, and seems not to have been updated since then.It is easy to navigate, with the first tab giving access to a full page-by-page facsimile, providing drop-down menus to allow quick retrieval of particular pages and feast days.An on/off tab highlights those pages containing chants that were recorded as part of the project (see below).Other tabs give access to a detailed codicological description, as well as to 14 extended video lectures (using Vimeo) dealing with different aspects of the book's gestation and the process of copying and decorating it; transcripts of the texts of these videos are included separately.The book's colophons name
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.048 |
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".