Cicero’s Incomplete Orator: The Transmission and Reception of the Mutilus Text
Bibliographic record
Abstract
This dissertation traces the tradition of Orator, Cicero’s late work on oratorical style, through the Middle Ages. During that time and due to mechanical losses, the text circulated in a reduced or mutilus form consisting of only the middle half and tail-end of the treatise. An early chapter (1) covers the tradition of the text as fragmentary quotations in other Classical and Late Antique authors. The core of my project, however, is a full codicological examination and catalogue (Appendix C) of the fifty-four surviving manuscript witnesses to this mutilus text. Proceeding from that research, I present the stemmatic relationships of the manuscripts, the geographic and chronological spread of the text, and the creation of two separate vulgate versions by early Italian humanists (Chapters 2 and 3). I present an edition of and commentary on a version of the text created by the early 15th c. schoolmaster Gasparino Barzizza, whose conjectures have long been praised by editors (Appendix A). I edit and classify the marginal and paratextual additions made by medieval readers to show how and why they read the text (Appendix B). Beyond the obvious contributions to textual criticism and the history of rhetoric, my dissertation demonstrates, through the lens of a single text, many of the various Ciceronianisms and Ciceros that existed in Latin intellectual history in the over a millennium and a half following his death.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".