Bibliographic record
Abstract
Many scholars have struggled to define and characterize the Late Roman Empire's defensive policy, in order to understand the underlying causes of the defensive deterioration, which some have seen as a factor in the fall of the Western Roman Empire. In 1976 American theorist Edward Luttwak published his Grand Strategy of the Roman Empire, which defined the defensive evolution of the third century as Rome's pragmatic shift towards a policy of 'defence in depth', whereby the military would abandon their traditionally static frontier security policy in exchange for a fundamentally elastic one. Was the Empire capable of designing a ‘grand strategy’, and did it have the command‐control capacity for such endeavours? Did the Romans commit a devastating strategic blunder by withdrawing the military presence that had for so long subdued the will of the migratory peoples in the periphery? Critics have accused this 'defence in depth' hypothesis of failing to factor in strong literary and archaeological evidence that contests the theory, while relying heavily on modern military concepts and terminology. Ongoing research has indicated that while this Late Roman strategy was executed in various forms throughout the empire, the essential approach of the military was dictated through a well defined operational framework. This paper asserts that Roman defence in depth was a consciously adopted policy of the Roman state and addresses some of the main criticisms presented by scholars who refute the possibility of this system's existence.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".