Bibliographic record
Abstract
Mobility in learned circles was a reality in the Europe of the Middle Ages, and it is only when we consider the reception of well-known works, such as the thirteenth-century Roman de la rose, in the countries where they circulated in the local language that we are able to gain a more complete understanding of their impact on literary and cultural currents even after the authors had passed away. Guillaume de Lorris and Jean de Meun’s conjoined Roman de la rose (1236, 1269-78) is without a doubt one of the foundational works of French medieval literature with over 360 extant manuscripts. Focusing on two non-French adaptations of this work that appeared within a century of the date of its composition, I show that these translations, or more accurately rewritings, enabled its survival and contributed to its sustained popularity in medieval Europe. The adaptations that are the subject of this analysis are Il Fiore, a thirteenth-century translation and adaptation into Italian often attributed to Dante, and the Romaunt of the Rose, commonly attributed to Geoffrey Chaucer. I conclude that through the medieval practice of interpretatio, the authors of the Fiore, and the Romaunt of the Rose adapt the original text to reflect their own contemporary cultural realities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".