Bibliographic record
Abstract
Definition of medieval romance as a genre has been bedevilled by the inability of scholars to reach a consensus on the essential features of romance. “Of all the dimensions of human experience explored by the romance genre, eros is by far the most essential,” remark Kevin Brownlee and Marina Scordilis Brownlee in their introduction to an important collection of essays by various scholars on the evolution of European medieval romances. Yet eros is neither mentioned nor implied in Paul Strohm’s report of consensus: “modern critics have a fairly tidy sense of the mediaeval romance as a narrative poem dealing with the adventures of a chivalric hero.” Nor is it mentioned in Newstead’s definition in the Wells-Severs Manual of the Writings in Middle English: “The medieval romance is a narrative about knightly prowess and adventure, inverse or in prose, intended primarily for the entertainment of a listening audience.” In part this difference is accounted for by the difference in bodies of romances being read: if the Brownlees are reading Chrétien and Le Roman de la rose while Strohm and Newstead are reading Havelok and the alliterative Morte Arthure, it is no wonder that the scholars of romance languages see eros as essential and the scholars of Middle English do not. But there is a fundamental disagreement between Strohm’s and Newstead’s definitions, too: Strohm disallows prose romances, such as Malory’s Morte Darthur, while Newstead allows them.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".