Women to the Forefront: A Case for Digital Medieval Prosopography
Bibliographic record
Abstract
Despite the growing number of medievalist projects, it is difficult to identify the ones that use Iberian chronicles to study people from the gender perspective in a digital setting. To bridge this gap, a prosopographical database of all women mentioned in the Crónica de Castilla (ca. 1300) has been produced. This article documents its development from the initial interests to the practical considerations of its published online version. On the one hand, carefully chosen examples illustrate various issues of the systematic approach, therefore firmly reminding us that the generated data sets are neither simply extracted nor neutral. On the other hand, the included visualizations and the preliminary observations help uncover new and engaging avenues for examining the women in the Crónica de Castilla and, by extension, in other historical narratives. Malgré le nombre croissant de projets médiévaux, il est difficile d’identifier ceux qui utilisent les chroniques ibériques pour étudier les individus sous l’angle du genre dans un cadre numérique. Pour combler cette lacune, une base de données prosopographique de toutes les femmes mentionnées dans la Crónica de Castilla (vers 1300) a été créée. Cet article documente son développement, depuis les intérêts initiaux jusqu’aux considérations pratiques la version qui a été publiée. D’une part, des exemples soigneusement choisis illustrent divers enjeux de l’approche systématique, rappelant ainsi fermement que les ensembles de données générés ne sont ni simplement extraits ni neutres. D’autre part, les visualisations incluses et les observations préliminaires permettent de découvrir de nouvelles pistes stimulantes pour examiner les femmes dans la Crónica de Castilla et, par extension, dans d’autres récits historiques.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".