Miriam J. Groen-Vallinga. <i>Work and Labour in the Cities of Roman Italy</i>.
Bibliographic record
Abstract
Miriam Groen-Vallinga’s study sof urban work in Roman Italy reflects a particular moment in the history of labor. In the later twentieth century, an interest in modern capitalism inspired a great deal of historical interest in labor, but mostly concentrating on recent periods of history. Over the last decades, the rise of global history has prompted not only geographically wider but temporally deeper interests in the topic. Witness Jan Lucassen’s sweeping recent history of work from the paleolithic to modernity (The Story of Work: A New History of Humankind [2022]). Groen-Vallinga’s study Work and Labour in the Cities of Roman Italy is a welcome effort to integrate Imperial Rome into these broader intellectual currents. The book is also timely because it follows on a wave of recent publications on all aspects of labor by Romanists themselves. Gone are the days when “primitivists” like Moses Finley held that slavery so marginalized all ancient labor, both free and slave, that the topic of work was hardly worth studying. Indeed, Groen-Vallinga starts off by dismissing the old debate about whether Roman labor was subject to market forces. It surely was, she argues, but the more important question is how that market was segmented. The legal division between free and slave constrained Roman labor in particular ways, as did age, gender, and skill. Showing how these divisions interacted to characterize Roman work, Groen-Vallinga frames her study around ideas of family and household. Roman households contained free, enslaved, and freed members of different ages, genders, and skillsets, and they deployed their members in response to supply and demand.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".