Bibliographic record
Abstract
LOCAL CONDITIONS REVISITED: A BRIEF SUMMARY The previous five chapters have brought the Second Punic War into focus from the perspective of the Italian states and suggest that Hannibal's lack of success as a diplomat was an important component of his overall defeat in the Italian theatre of the war. Because Rome enjoyed a significant manpower advantage, Hannibal needed to elicit massive allied revolts in a short period of time. Rome's Italian allies were willing to come over to Hannibal's side, but only on their own terms, and Hannibal struggled to get all the communities in any given region to revolt at the same time. Moreover, it was difficult for Hannibal to maintain the loyalty of the Italian communities that did revolt. The arguments presented in this book reveal that local conditions and motivations significantly influenced the decisions of various Italian states to remain loyal to Rome, thus shaping the course and ultimately the outcome of the Second Punic War. In short, Hannibal's failure resulted from military disadvantage that he could not overcome through diplomatic means because of local, circumstantial factors. Why was Hannibal unable to unify the Italians against Rome, or even to keep his new Italian allies unified during the eventual war of attrition? Goldsworthy has stated that the communities that did join Hannibal lacked a sense of common identity or purpose. This was indeed the case, though it is perhaps more accurate to say that there were too many mutually exclusive identities and agendas.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.241 | 0.079 |
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".