Teaching Digital Byzantine Sigillography: First Experiences and Future Strategies
Bibliographic record
Abstract
This paper delves into the preliminary results and future initiatives concerning the pedagogical aspects of two funded projects, DigiByzSeal (supported by the Deutsche Forschungsgemeinschaft and the Agence Nationale de la Recherche) and DiBS (funded by the VolkswagenStiftung), which jointly aim to advance the field of Byzantine Sigillography. One of the objectives of these projects that stands out most prominently is the establishment of a sustainable, research-based, digital teaching infrastructure, along with the introduction of innovative pedagogical methods. In this paper, we specifically scrutinize two distinct teaching formats: (1) SigiDoc training weeks, designed to equip experts in Byzantine Sigillography with proficiency in XML and data modelling, and (2) an international seminar centred around the creation of a permanent digital exhibition addressing various facets of Byzantine society through the lens of seals. These instructional approaches present both organizational and conceptual complexities. However, the overarching aim in both cases is to optimize data reuse for sustainability, accessibility, and informed utilization. Furthermore, this paper touches upon the implementation of collaborative digital strategies pertaining to Byzantine artefacts containing textual elements. It underscores the cultivation of interdisciplinary exchanges with the field of Digital Humanities and the integration of globally shared pedagogical concepts within Byzantine Sigillography and Byzantine Studies at large.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".