Sallust’s Motivation and Cicero’s Influence in the Writing of the Bellum Catilinae
Bibliographic record
Abstract
In 80 BC, at the age of 26, the future Roman statesman Marcus Tullius Cicero defended one Sextus Roscius from accusations of patricide. For Cicero, the stakes were high for challenging such a strong accusation, as patricide was seen as a horrific crime in the public eye of Rome. For one, if Cicero were to lose his defense, he would be the one to blame for Roscius’ consequential harsh punishment, Poena Cullei. Reserved only for patricide, this type of sentence involved wrapping the perpetrator’s head in wolf skin and their beaten body sewn into a sack with live animals—namely snakes, dogs, chickens, and monkeys; only then was the body bag thrown into the water, preventing the traditional and honorable burial that most Romans had.1 Furthermore, Cicero decided to blame the murder on some men with close relations to Sulla, the dictator of the republic and an influential man easily able to silence him. Ultimately, the amateur lawyer won his first public case and used its high stakes to bring himself public recognition. Cicero acknowledges this in one of his works: Itaque prima causa publica pro Sex. Roscio dicta tantum commendationis habuit ut non ulla esset quae non digna nostro patrocinio videretur (“My defense of Sex. Roscius, which was the first public cause I pleaded, met with such a favorable reception, that I was looked upon as an advocate of the first class, and equal to the greatest and most important causes”).2 This fame kickstarted Cicero’s public career, facilitated his rise to consulship in 63 BC, and foreshadowed one of the most notable events of his political career: the Catilinarian conspiracy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".