Bibliographic record
Abstract
This study provides an updated synthesis of the long-standing debate regarding the authorship of the Fiore and the Detto d’Amore, tradition- ally attributed to Dante Alighieri. While the paradigm established by Gianfranco Contini dominated the 20th century, recent philological, codicological, and linguistic evidence has significantly undermined this attribution. Key arguments include the reinterpretation of the name Durante as a speaking name rather than an authorial signa- ture, the French material origin of the Montpellier manuscript, and a complex linguistic stratigraphy that suggests a dynamic textual tradition involving multiple Tuscan areas. The research integrates these findings with advanced computational analysis and Artificial Intelligence. Using stylometric methods such as Support Vector Machines (SVM) and Burrows’ Delta, the study demonstrates a de- finitive statistical distance between the Fiore and Dante’s authentic corpus. Conversely, the data reveals a strong stylistic proximity to the comic-realistic tradition, particularly the works of Rustico Filippi and Cecco Angiolieri. Ultimately, the essay proposes that the author was likely a talented, bilingual Florentine poet active in the circle of Brunetto Latini among Guelph exiles in France.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".