The depiction of Livia and Julia Domna by Cassius Dio: Some observations
Bibliographic record
Abstract
Among the ancient authors who narrated the reign of Augustus and Tiberius, Cassius Dio is surely the one who dedicated the most space to the influence that Livia Drusilla exercised over both her husband and her son. In this regard, the foremost example is found in a large section where Dio narrates how Livia persuaded Augustus to forgive Cornelius Cinna for having plotted against his regime. Also, according to Dio, after the death of Augustus, Livia considerably increased her authority over the imperial government, trying not only to co-rule with her son, but also to become the sole effective ruler by controlling all his political activities. Some scholars have suggested that Dio probably exaggerated the role played by Livia because of the similar extraordinary power enjoyed by his contemporaries Julia Domna and the other Syrian women who lived during the Severan age. A close examination of Dio’s passages dedicated to Livia reveals no traces of situations that could refer to his contemporary political situation. The statements of the Bithynian historian and senator concerning Livia are normally well detailed because he made use of good sources. Indubitably, Livia’s strong influence was fundamental in shaping the reign of both Augustus and Tiberius. Even two centuries later, while Severus was trying to depict his regime as a new golden era on the model of Augustus, Julia Domna followed the example of Livia on many occasions. Nevertheless, Dio does not seem to be aware of these analogies and his work appears to be characterized by a mere record of facts rather than an investigation of their real power within the imperial court.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".