Clementina Caputo and Julia Lougovaya, Using Ostraca in the Ancient World
Bibliographic record
Abstract
This remarkable volume provides the richest introduction ever offered to one of the most widespread but understudied writing technologies of the ancient world. Like many such categories, ostraca are a somewhat fuzzy set, and the term “ostracon” is often used imprecisely. Properly speaking, the ostracon is a potsherd or, sometimes, a piece of stone, in a secondary use (i.e., not its original purpose) as a writing surface. But various other objects get included from time to time because they do not form a recognized category of their own, and writing on pottery as part of the primary use of a vessel is sometimes distinguished from ostraca as jar inscriptions and sometimes not. Ostraca were for long treated with disdain or positive horror by most papyrologists, but they have increasingly come into their own; and this set of chapters, based on a conference, represents a kind of coming of age of the study of ostraca. Reviewed by: Roger S. Bagnall, Published Online (2022-07-31)Copyright © 2022 by Roger S. Bagnall Article PDF Link: https://jps.library.utoronto.ca/index.php/aestimatio/article/view/39090/29779 Corresponding Author: Roger S. Bagnall,New York UniversityE-Mail: roger.bagnall@nyu.edu
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".