Creating a Sigillographic Search Engine for Byzantium: Preliminary Results
Bibliographic record
Abstract
Many of the documents that were created in the Byzantine Empire (the former Eastern Roman Empire, 4th–15th centuries) are no longer extant, but the seals that accompanied the documents have survived in large numbers, providing information for various research fields within Byzantine studies. The ANR/DFG project DigiByzSeal aims to use digital presentation to enable new understandings of Byzantium and its written culture by transforming Byzantine sigillography. The project focuses on SigiDoc, which provides an XML-based and EpiDoc-compliant data model for the digital scholarly edition of Byzantine seals. The DigiByzSeal team is working on the first scholarly digital edition of approximately 4,000 seals, which will be freely accessible and based on open-source software. The goal is to create a centralized hub for Byzantine sigillography, with a unified federated search interface for all seals encoded with SigiDoc, based on a highly customized and enhanced instance of EFES (EpiDoc Front-End Services). This paper presents the preliminary results of the development of SigiDoc, and of the unified search interface based on it, and discusses the methodology and the challenges faced thus far, while showing how the project promotes open, shared, and accessible information and overcomes the issue of accessibility and lack of interoperability in Byzantine sigillography.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".