Function and Use of Roman Pottery: A Quantitative Method for Assessing Use-Wear
Bibliographic record
Abstract
The study of ceramic function, use, and re-use is becoming an important facet of the archaeology of the ancient Mediterranean. The abundant and detailed ceramic typologies of classical archaeology have meant that the function of pottery is typically assumed based on vessel form. I propose a way of nuancing our understanding of ceramic function and use to reveal repetitive habitual actions: the traces of seemingly ephemeral behaviors of daily life. I introduce a method for the empirical study of ceramic use through the application of ‘alteration analysis’ (more commonly known as ‘use-wear analysis’): that is, the recording of accretion and attrition of the surfaces of domestic pottery. I describe a recording method to capture qualitative observations of pottery in a quantitative way. This approach allows for the generation of relatively large datasets that can be subjected to statistical analysis in order to observe real patterns of use. We can begin to make inferences about behaviors like cooking and serving, the length of ceramic use-life, and the extent of ceramic consumption. I demonstrate the utility of this method with a case study of ceramics from two Republican-period sites in central Italy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".