Galbert of Bruges and the Historiography of Medieval Flanders
Bibliographic record
Abstract
Galbert of Bruges's The Murder, Betrayal and Assassination of the Glorious Charles, Count of Flanders is one of the most widely read books of the Middle Ages. It recounts the assassination of Charles, count of Flanders, and the events leading up to and following the murder. Galbert was a resident of Bruges and had served in the count's administration for at least thirteen years by the time of the assassination in 1127. He was well-acquainted with Charles and many of the other actors in this drama, an eyewitness to many of the events he relates, and exceptionally well positioned to gather information about others. Galbert's chronicle takes the form of a journal, the only one that exists from northwestern Europe in the twelfth century. Edited by two of the world's most prominent specialists on Galbert today, Jeff Rider and Alan V. Murray, this book brings together essays by established scholars who have been largely responsible for the radical changes in the understanding of Galbert and his work that have occurred over the last thirty years and essays by younger scholars. The essays are written by British, Belgian, Dutch, German, Canadian, and American scholars of literature and history, and are divided into four sections--Galbert of Bruges at Work, Galbert of Bruges and the Development of Institutions, Galbert of Bruges and the Politics of Gender, and The Meanings of History. The book includes an extensive bibliography of editions, translations, and studies of Galbert's chronicle, and of works devoted to the reign of Charles the Good and the Flemish Crisis of 1127-28, to the government and institutions of Flanders in the age of Galbert, and to the topography and history of medieval Bruges. In addition to the editors, the contributors are Lisa H. Cooper, University of Wisconsin-Madison; Godfried Croenen, the University of Liverpool Centre for Medieval and Renaissance Studies; Bert Demyttenaere, University of Amsterdam; Mary Agnes Edsall, Bowdoin College; Martina Häcker, University of Mainz; Dirk Heirbaut, University of Ghent; Steven Isaac, Longwood University; Nancy F. Partner, McGill University; Robert M. Stein, Purchase College and Columbia University; and R. C. van Caenegem, University of Ghent.
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 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".