Bibliographic record
Abstract
In this book, Catherine E. Pratt explores how oil and wine became increasingly entangled in Greek culture, from the Late Bronze Age to the Archaic period. Using ceramic, architectural, and archaeobotanical data, she argues that Bronze Age exchange practices initiated a strong network of dependency between oil and wine production, and the people who produced, exchanged, and used them. After the palatial collapse, these prehistoric connections intensified during the Iron Age and evolved into the large-scale industries of the Classical period. Pratt argues that oil and wine in pre-Classical Greece should be considered 'cultural commodities', products that become indispensable for proper social and economic exchanges well beyond economic advantage. Offering a detailed diachronic account of the changing roles of surplus oil and wine in the economies of pre-classical Greek societies, her book contributes to a broader understanding of the complex interconnections between agriculture, commerce, and culture in the ancient Mediterranean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".