Women to the Forefront: A Case for Digital Medieval Prosopography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it