Bibliographic record
Abstract
1. Introduction (Gale R. Owen-Crocker, Professor of Anglo-Saxon Culture at the University of Manchester, UK and Director of the AHRC-funded project 'The Lexis of Cloth and Clothing in Britain c. 700-1450' and Maria Carmela Cesario, Lecturer in Medieval English Language and Literature at Brasenose College, University of Oxford, UK) 2. The construction and writing of Anglo-Saxon manuscripts (Alexander Rumble, Reader in Palaeography and Director of the Manchester Centre for Anglo-Saxon Studies at the University of Manchester, UK) 3. Manuscript sources of Old English prose (Donald Scragg, Emeritus Professor of Anglo-Saxon Studies at the University of Manchester, UK 4. Manuscript sources of Old English poetry (Elaine Treharne, Professor of Medieval Literature at Florida State University, USA and Co-Director of the AHRC-funded project 'The Production and Use of English Manuscripts 1060-1220') 5. A survey of Latin manuscripts (Gernot Wieland, Professor of English at the University of British Columbia, Canada) 6. Reading between (and beyond) the lines: glosses and notes in Anglo-Saxon manuscripts (Timothy Graham, Associate Professor of History and Director of the Institute for Medieval Studies, University of New Mexico, USA) 7. Manuscript art (Catherine Karkov, Professor of Art History at the University of Leeds, UK) 8. From manuscript to computer (Stuart Lee, Acting Director of University Computing Services and a teaching member of the English Faculty at the University of Oxford, UK and Daniel O'Donnell, Associate Professor of English, Chair of the Text Encoding Initiative and Director of the Digital Medievalist Project, University of Lethbridge, Canada) Glossary Index of Manuscripts Index
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.051 |
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".