Dialogical Strategies, <i>Volgarizzamento</i>, and Ciceronian <i>Ethos</i> in Antonio Brucioli's <i>Dialogi Della Morale Filosofia</i>
Bibliographic record
Abstract
In 1526, Antonio Brucioli (1487-1566) published a series of thirty dialogues that form a typical humanist compendium of moral and political wisdom in the vernacular. Scholars have considered these dialogues mainly from the perspective of Brucioli’s humanist and political allegiances, in particular with exiled Florentine humanists whose discussions in the Orti Oricellari the dialogues echo. However, textual reworkings in subsequent editions (1538 and 1544, in which this group constitutes the first volume, entitled Della morale filosofia, of a series that includes other volumes of dialogues on natural philosophy) warrant a reconsideration that complements intellectual history with literary and rhetorical analysis. This article revalorizes Brucioli’s Dialogi della morale filosofia by arguing that their literary and rhetorical strategies, such as the use of ancient dialogue models, the shifting choice and staging of interlocutors, the creation of Ciceronian ethos and decorum, and the mimetic aspects of the interaction of male and female voices not only evince a conscious application of early Cinquecento dialogue poetics, but also establish the author’s volgarizzamento of a compendium of classical and humanist wisdom as a uniquely Italian project aimed at an emulation and appropriation of moral philosophy by dialogical speaking at the level of a national cultural elite.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 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".