Eco’s <i>The Name of the Rose</i> : Bricolage and montage of cultural history
Bibliographic record
Abstract
Umberto Eco loved analogies, was an artist of the déjà-vu and a great bricoleur in architecting a pastiche of genre and a collage of intertextual anxieties of influences. I begin with his inspiration from one of his favorite and most influential writers, James Joyce, and will close with the influence of his all-time favorite movie, Casablanca. Eco’s remarkable study of Joyce’s works appeared in the first edition of Opera aperta. The influence of this meticulous analysis of puns, riddles, metonymy, and interactive metaphors resurfaces with his encyclopedic knowledge in The Name of the Rose. My argument is that Eco, keeping the famous Irish writer in mind, structured his own novels as dynamic epistemological metaphors. In addition to the skillful use of parodies, irony, puns, metaphors, erudition, semiosis, details, and comic relief, The Name of the Rose reveals many of Eco’s narrative strategies. Socratic dialogues, intertextual frames, citations, palimpsests, and chains of associations are at the center of his possible world of fiction where History and stories intertwine. From the cult movie Casablanca Eco learned how an intertextual collage of clichés was used constructively: “two clichés make us laugh, but a hundred clichés move us because we sense dimly that the clichés are talking among themselves and celebrating a reunion.” In The Rose we encounter clichés, archetypes, and familiar quotations that with Eco’s encyclopedic knowledge contribute to making his first hybrid cognitive narrative an excellent example of docere et delectare.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".