Back matter (“Michael W. Herren: Bibliography, 1963–2006”, “Index I: Manuscripts”, “Index II: Authors, People, Places, and Texts”)
Bibliographic record
Abstract
Some thirty years ago Michael Herren burst on the medieval Latin scene with his edition and translation of the notoriously difficult Hisperica Famina, and followed this a few years later with his translation of the prose works of Aldhelm. Notice was given that a junior scholar, unafraid to tackle some of the most obscure, complex, and arcane Latin, wished to make it accessible to non-Latinists as well as to those Latinists who lacked his particular skills. Not content with labouring alone in that field, Herren gathered scholars in Toronto to a conference on “Insular Latin Studies,” the proceedings of which he published two years later. Over the years he shed considerable light on such obscure texts and authors as Virgilius Maro Grammaticus, John Scottus Eriugena, and the Cosmographia by the pseudonymous Aethicus Ister. His research trail led him again and again to Ireland, and the Irish contribution to early medieval Latinity and to English, Carolingian, and even Italian culture. Recognizing the rich diversity of medieval Latin, Herren in 1990 founded The Journal of Medieval Latin and has, as its editor, provided a home for medieval Latinists of all stripes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.184 | 0.148 |
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".