Making Stemmas with Small Samples, and New Media Approaches to Publishing them: Testing the Stemma of Konráðs saga keisarasonar
Why this work is in the frame
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Bibliographic record
Abstract
Data associated with the <em>Digital Medievalist</em> article 'Making Stemmas with Small Samples, and New Media Approaches to Publishing them: Testing the Stemma of <em>Konráðs saga keisarasonar</em>'. With relatively few scholars and a large number of texts whose manuscript transmission has yet to be mapped, Icelandic literature would benefit from efficient ways of establishing stemmas, to facilitate the study of literature, linguistics, scribal culture, and so Icelandic history more generally. This is also true for much medieval literature. Meanwhile, in saga-studies as in stemmatology generally, there has been little discussion of the role of sampling in textual criticism, even though most scholars must make heavy use of it. This article tests the viability of creating a stemma using a small sample of text by independently drawing a stemma of <em>Konráðs saga keisarasonar</em>, whose stemma was previously established by Zitzelsberger, and testing it against Zitzelsberger's. Although the approach has limitations, at worst it produces known unknowns which can then be resolved through targeted study; in practice it produces results very similar to Zitzelsberger's; and in some cases it actually allows us to improve on his work. The article also capitalises on internet publication rigorously to include all underlying data and to experiment with new, more transparent, ways of publishing stemmas; and to use digitised data to provide a new overview of the long manuscript tradition of medieval Icelandic romance sagas. Finally, it describes and filiates two new manuscripts of the saga identified in Winnipeg by Katelin Parsons. It concludes by sketching what the stemma of <em>Konráðs saga</em> can tell us about Icelandic scribal culture during its long post-medieval history.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.000 |
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 it