Clustering of medieval scripts through computer image analysis: Towards an evaluation protocol
Bibliographic record
Abstract
This paper addresses the question of "objective" categories of medieval scripts and their elaboration through both medieval palaeography and image analysis. It introduces a dataset of 9800 images and metadata from the catalogues of dated manuscripts in France, as a "ground truth" and evaluation protocol, to be used for image feature analysis, taxonomy building, and clustering methods. It further compares the results of the categorization performed by two teams, one in Lyon (LIRIS/INSA, Frank Lebourgeois) and the other in Tel-Aviv (The Blavatnik School of Computer Science at Tel Aviv University, Lior Wolf). It also addresses the questions of taxonomy, interpretation and goals of the interdisciplinary research, such as development of "expert systems" or exploratory research.
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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 | 0.133 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".