Bibliographic record
Abstract
In July 1552 the city of Siena rebelled against its Spanish overlords that had either influenced or directed the Republic’s government for several years, threw out the Spanish garrison that controlled the city, and open its doors to a French army sent by King Henri II to protect the city and bring it into the French sphere of influence. The man in charge of this army was the French marshal Blaise de Monluc (1500–1577), who arrived shortly after the anti-Spanish insurrection and remained until the end of the siege when the city, exhausted and depleted, finally surrendered to a mercenary army hired by the duke of Florence, Cosimo I de’ Medici, on behalf of Emperor Charles V von Habsburg. In his memoires of his military campaigns, Blaise de Monluc recalls his Sienese years and especially the valour of the women of Siena who contributed in no small way to the defence of their city. This article outlines the events before and during the siege and Monluc’s comments about the women of Siena. It then analyses these comments in order to gain a better understanding of what, exactly, the women did for their city and who these women were. En juillet 1552, la ville de Sienne se rebella contre ses suzerains espagnols qui avaient soit influencé ou dirigé le gouvernement de la république pendant plusieurs années ; elle mit en fuite la garnison espagnole qui contrôlait la ville et ouvrit ses portes à une armée française envoyée par le roi Henri II pour la protéger et l’intégrer dans la sphère d’influence française. L’homme chargé de cette armée était le maréchal français Blaise de Monluc (1500–1577), qui arriva peu de temps après l’insurrection anti-espagnole et resta jusqu’à la fin du siège lorsque la ville, épuisée, finit par se rendre à une armée mercenaire embauchée par le duc de Florence, Côsme Ier de Médicis, au nom de l’empereur Charles V de Habsbourg. Dans les mémoires de ses campagnes militaires, Blaise de Monluc se rappelle de ses années à Sienne et surtout du courage des femmes siennoises qui contribuèrent de manière non négligeable à la défense de leur ville. Cet article donne un aperçu des événements avant et pendant le siège ainsi que des commentaires de Monluc sur les femmes de Sienne. Ensuite, il analyse ces commentaires afin d’acquérir une meilleure compréhension de ce que les femmes ont fait pour leur ville et qui elles étaient.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".