Bibliographic record
Abstract
Ancient Rome was an expansive and wealthy empire that created a trading network that relied on common language(s), law, money, and a system of measurement. An important component of this network was the rectangular system (RS) of land demarcation known as centuriation, which was the forerunner of similar systems adopted in the United States and Canada and in other parts of the British Empire in the eighteenth and nineteenth centuries. This purposefully implemented demarcation system persists to the present and can be found in the landscape throughout the territories of the former Empire, especially in Italy and North Africa. It was typical as Rome expanded its territories to implement the RS system before new lands were settled by Romans. There was considerable variation in land demarcation patterns across the Empire. This chapter examines the determinants of centuriation by describing a model in which the state chooses between adopting RS to demarcate new lands or to utilize existing demarcation. The chapter examines data on centuriated areas and accounts from archeologists and classics scholars to examine the economic structure of Roman centuriation. The chapter generally finds that centuriation was adopted in flatter, more fertile lands, and later in time as survey techniques improved and administrative structures were expanded. The chapter also finds that alignment tended to be perpendicular to rivers and streams in order to minimize demarcation costs and to facilitate a network of canals for drainage. Finally, the chapter provides conjectures about the impact on centuriation on development and growth within the Empire.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".