Les maladies féminines dans l’Espagne du XIXe siècle : spécificités et discours normatif
Bibliographic record
Abstract
Cette étude prend appui sur un traité manuscrit daté de 1817, qui est manifestement une synthèse des cours dispensés par un chirurgien espagnol, Pedro Castelló (1770-1850), professeur au Collège de chirurgie médicale de Madrid. Ce cours prétendait former les futurs chirurgiens et médecins aux maladies dites féminines, et on peut considérer qu’il marque les débuts de la gynécologie, en tant que spécialité médicale. Au-delà de l’intérêt scientifique du document pour une histoire de la médecine, ce manuscrit est révélateur de la vision genrée de la maladie qui s’impose en ce début de XIXe siècle, et qui fait de la femme un être au corps « naturellement » déréglé, une éternelle malade qu’il faut traiter afin qu’elle puisse pleinement jouer son rôle de procréatrice. Cette étude s’emploie à analyser comment la volonté de codification et de transmission des savoirs vient réactualiser le discours moral traditionnel pour lui donner une dimension sociale et idéologique. En s’appuyant sur les médecins et les chirurgiens -obstétriciens, l’Etat veille à ce que les femmes aient de nombreux enfants en bonne santé, mais l’objectif est avant tout de les persuader d’accepter de bonne grâce ce qui est présenté non plus comme une simple fonction naturelle, mais comme une véritable fonction sociale et économique, dans une approche clairement politique de la santé.This study is based on a manuscript treatise dated 1817 which is clearly a synthesis of classes provided by Pedro Castello, a Spanish surgeon (1770-1850), teacher at Madrid medical surgery school. This course professed to train future surgeons and doctors of so-called 'feminine' diseases and it can be considered as a starting point for gynecology as a medical field. Beyond the document's scientific interest for a history of medicine, this manuscript reveals the gendered vision of disease which imposes itself at the beginning of 19th century and which makes women, human beings with a «naturally» disturbed body, everlastingly ill who need to be treated so that they can fully play their reproductive role. This study strives to analyse how the will to codify and transmit knowledge contributed to refresh traditional moral speech as to give it social and ideological dimension. Based on doctors, surgeons and obstetricians, the State ensures that women have many healthy children; however above all the aim is to persuade them to willingly accept that fate, which is no longer presented as a mere natural function but as a real social and economic function, in a clear health political approach.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".