Writing Labor History Today: A Critical Note on The Case of the Roman Empire
Bibliographic record
Abstract
Three recently published books raise the question of labor in the Roman Empire. The present article aims to investigate the sources privileged by historians, the scale of observation on which their analysis is situated, and the theoretical assumptions that guide them. These reflections show that there are multiple ways of writing labor history, currently divided into different subfields which do not always communicate with one another. Thanks to new readings of ancient literature and epigraphy, and the contribution of papyri and archaeology, the traditional history of work and trades has been widely renewed. An important line of questioning examines the reasons for the high degree of trade specialization in the Roman Empire, as well as the existence of a true division of labor. Archaeology helps us understand the technologies and processes of production, making it possible to establish a typology of the socioprofessional identities, from employers to employees, that existed in the shops and workshops of the Roman world. A quite different approach investigates the organization of labor from a macroeconomic perspective, seeing it as a force mobilized by employers: comparisons between the productivity of slaves and that of free workers have been replaced by analyses of the transaction costs of free hired labor versus servile manpower. Finally, debate continues between historians who consider that the labor market of the Roman Empire was limited by clientelist networks and servile labor, and those who describe a free-market economy where labor had become a commodity.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.030 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".