Bibliographic record
Abstract
W ith the opening of stable trade routes in the early Empire, Romans took a sustained scholarly interest in South Asian topography.The abundance and exoticism of the goods of the East provided a visual template for paradise, but, surprisingly, Roman writers ascribe prestige to indirect market transactions rather than direct military control.Distance heightened both what was desirable and forbidding about the East.I argue that the impossibility of a monopoly over the eastern luxury trade led to a rethinking of the hard and soft borders of empire: where direct control was impossible or impractical, the study of topography "confirms" that it was also undesirable.The topographers' defense of trade directly affects their choice and use of sources, and the result is a value-laden topography of extremity that affirms several foregone conclusions, notably the centrality and primacy of the Mediterranean and its "natural" limits.For Roman encyclopedists such as Strabo and Pliny, topography shows that caravans, not campaigns, define the ideal limits of Roman power so that markets, not fortresses, become the "good end of empire." Roman Economy and TopographyRomans encountered India in waves of military and trade relations.The discovery of the monsoon winds by Hippalus around the first century bce made it possible to sail from the Red Sea to India, and by the time of Augustus Roman ships were skirting the Malabar coast. 1 Overland trade persisted alongside the new route despite being comparatively arduous, and * My thanks to Andrew Ollett and the anonymous reviewer for Classical Antiquity; remaining mistakes are my own.Translations follow the Loeb Classical Library except where noted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".