Nationcraft and the Origins of Territory: Experiencing Romanía in the Medieval Empire of New Rome
Bibliographic record
Abstract
Abstract The modernism debate in the historiography of nationhood and nationalism has fizzled out to a curious détente: the idea that nationhood and nationalism are unique to ‘modernity’ remains dominant, but ‘premodern’ fields continue to research ethnonational phenomena while largely avoiding the vocabulary. Compelling research continues to be produced on both sides of the pre/modern divide, but there is little cross-fertilization between the two. This article returns to the modernism debate, to argue for the utility of political economy as a mode of analysis able to address the dynamics of nationcraft across a range of times and places. The case study is the production and experience of national territory in the medieval empire of New Rome, traditionally termed Byzantium. Between the eighth and thirteenth centuries East Roman political economy produced a national territory known as Romanía, ‘Romanland’, experienced for the most part in terms strikingly similar to the ‘countries’ produced by contemporary nation-states, including a kind of patriotism. The implication, fleshed out with comparative suggestions in the conclusion, is that similarities and differences between the nationcraft of different times and places should be situated in political and economic motions, rather than a pre/modern binary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".