Enabling the Encoding of Manuscripts within the DTABf: Extension and Modularization of the Format
Bibliographic record
Abstract
This paper presents work in progress on the DTA “Base Format” for Manuscripts (DTABf-M), an extension to the DTA “Base Format” (DTABf) for the TEI-conformant annotation of manuscripts. The DTABf is a TEI-subset for the consistent, yet unambiguous, annotation of large amounts of historical text. During our work on the DTA corpora, the DTABf has continuously been subject to further adaptations to specific annotation needs. The latest addition, the DTABf-M, contains elements, attributes, and values necessary for the annotation of (historical) handwritten documents. The goal is to provide a TEI format for diverse manuscripts in large text corpora. While the DTABf covers a wide range of phenomena found not only in printed texts but also in manuscripts, there are certain manuscript-specific features which have to be additionally represented by the DTABf-M. There are several prerequisites for DTABf-M to be suitable for the DTA and its workflows and processes: First, it should be based on the original DTABf tagset, and only extend it if unavoidable. Second, like the DTABf, the DTABf-M should be created in a bottom-up approach, that is, based on actual phenomena found in handwritten texts which are transcribed and encoded using the DTABf. Third, the format should complement the DTABf, not replace it. Hence, it is necessary to find a modular way of integrating the DTABf-M into the DTABf. This paper describes how we deal with these issues in the process of developing the DTABf-M.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.016 |
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".