Antony's Letter to Hyrcanus and the Battle of Philippi
Bibliographic record
Abstract
This thesis introduces a letter from Mark Antony thus far absent from the scholarly discussion on the Battle of Philippi. The introduction of this letter helps to correctly situate the battle and better understand how the narrative writers on the battle interacted with the tradition. Chapter 1 follows the narrative history of the battle and provides the context required to understand how the armies of the Caesarians and Liberators met and then fought in October of 42 BC. A discussion of the previous scholarship follows. Chapter 2 aims to understand what makes Appian different, and in his difference, how did he impact our understanding of the battle. This chapter reveals that Appian made a mistake in his understanding of the geography, but, as a skilled writer, created an internally consistent narrative. This fact has shaped our understanding of the battle for over a century. Chapter 3 argues for Antony to take Appian’s place. This Chapter begins with arguments for understanding Antony’s letter as authentic and follows it with an analysis of each narrative on Philippi in light of what Antony said about the geography. As a result, Antony’s letter should now take the principal seat from Appian, whose account, although tactically sound, does not reflect the geography and must be set aside.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".