Bibliographic record
Abstract
Abstract Historians of premodern economies, in contrast to modern ones, have only infrequently contemplated the economic contribution of slavery. Here, I suggest that quantitative and statistical tools allow us to evaluate the place of slavery in an early economy, using Roman Pompeii as a case study. At the time of its destruction in 79 ce, Pompeii appears prosperous, having benefitted from the economic development thought to have characterized the Roman world. Recent discoveries, meanwhile, shed new light on the conditions of working classes and slaves throughout the city. These narratives can be seen to form two sides to the same coin, as Pompeii’s prosperity was created in large part thanks to slave labour. The connection is supported by constructing a probabilistic model, which suggests some 6 million sesterces (HS) flowed every year to Pompeii’s masters through their exploitation of slaves. Slave owning probably formed the largest single income source for the urban economy. This scale of income is shown to be consistent with recent reconstructions of wealth and income inequality in the city. The results not only speak to slavery’s profound importance to Pompeii’s prosperity, but they encourage a recentring of labour and slavery in Roman economic history.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".