Bibliographic record
Abstract
Hannibal achieved some measure of success in eliciting defections from among the Samnites, especially in southern and western Samnium (the lands of the Hirpini and Caudini, respectively). Several communities of the Hirpini came over to Hannibal in the immediate wake of the battle of Cannae. According to Livy (23.1.1–3), Hannibal was invited to Compsa, which then fell into his hands peacefully. After this, Hannibal placed part of his army under the command of Mago, whom ‘he ordered either to receive the cities of this region that were then defecting from the Romans, or to compel those to defect that were refusing to’. The passage clearly illustrates that other Hirpinian communities began to fall away from Rome at about the same time as Compsa. In 215 the Romans reportedly conducted raids against the Hirpini in the vicinity of Nola, obviously against towns that had defected. It is likely that they had rebelled in the previous year. Besides Compsa, the names of only a few rebellious Hirpinian towns are known: Vercellium, Vescellium, Sicilinum, Meles and Marmoreae. Similarly, we hear of the Romans capturing towns that belonged to the Caudini or laying waste to their territory, indicating that several had defected. M. Claudius Marcellus and Q. Fabius Maximus conducted campaigns in the vicinity of Caudium in 215 and 214, during which the Romans took Compulteria/Conpulteria, Trebula Balliensis, Austicula and Telesia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".