The language and copying practices of three early \nmedieval cartulary scribes At Worcester
Bibliographic record
Abstract
This thesis investigates the factors that influence the ways in which scribes copied Old \nEnglish in charter texts. These factors include: the training scribes received in learning to \nwrite Old English and to copy texts; the role of the Anglo-Saxon scriptorium and the \nenvironment in which scribes worked; and the role of training and scriptorial influence in \nthe development of a scribe’s written system. This investigation has highlighted, in \nparticular, the lack of information about how scribes were trained in Old English compared \nto what is known of their training in Latin and in script acquisition. \nTo investigate these factors, this thesis uses a comparative study of the work of the \nscribe of the eleventh-century Worcester Nero Middleton cartulary, copying the texts S \n1280 and S 1556 from the early eleventh-century cartulary Liber Wigorniensis. The data is \ntaken directly from the manuscripts and from original transcriptions of each charter copy, \nwhich provides evidence not available in editions. \nThis study demonstrates the worth of studying later copies of texts, in particular of \ncharters. It also shows the wealth of information to be found in the work of copying \nscribes. The study of the Nero Middleton scribe’s work has shown that scribal copying is \nnot simply the application of one system (the copying scribe’s) onto another (the \nexemplar’s). In the two texts studied, this scribe exhibits different behaviours, varying in \nways which are not the result of influence from their exemplar, but which suggest that their \ncopying style and written system is changeable. From this it can be concluded that the \nscribes underwent some training in writing Old English which formalized aspects of their \nwritten conventions, but that much of the scribes’ conventions appear to have been \ninfluenced by the collaborative environment of the scriptorium in which they worked.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".