The Problem of Look-Alike Characters in the<i>Vulgate Cycle of the Arthurian Romances</i>and Juan Manuel's<i>El Conde Lucanor</i>
Bibliographic record
Abstract
Medieval people knew as well as we do that no two people could look exactly alike. But the question of look-alikes was nevertheless a fascinating one because it opened up a potential gap between external manifestation and deeper identity. What did it mean for the ontology of the self if one person could be substituted for another with nobody being the wiser? This article examines two medieval reflections on the possibility of an absolute resemblance that enables one character to be substituted for another. The first is the case of the “False Guenevere” in the Lancelot section of the enormous thirteenth-century Vulgate Cycle of the Arthurian romances, and the second is from Spain: El Conde Lucanor by the great fourteenth-century writer Don Juan Manuel. The two instances contrast nicely. In the first, Queen Guenevere's look-alike half-sister arrives at Arthur's court with the false claim that she is the true Guenevere and that the sitting queen has usurped her place. Indeed, she triumphs temporarily until Lancelot restores the proper order through trial by combat. In the second, an angel counterfeits a king's physiognomy (after the king commits blasphemy) and dresses in his clothes, leaving him only a pauper's vestments. In the Arthurian tale, the problem posed is how to know the true inner self when two identical-looking women both claim to be the “true” queen — a question that is complicated by the fact that the “true” Guenevere has been “false” for many years due to her adulterous affair with Lancelot. Conversely, Juan Manuel poses the question of identity as one of context: when the king is deprived of the trappings of his kingship, no one recognizes him. Both texts point up the problems of signification and identity that haunted medieval thinking. What is the relationship between one's outer appearance and inner nature? What is the relationship between a sign and what it signifies?
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.047 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".