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Record W2998051245 · doi:10.29173/cf504

La matrice ou le drageoir aux transgressions Lectures croisées des pathologies féminines fin-de-siècle

2019· article· fr· W2998051245 on OpenAlexvenueno aff
Laure-Hélène Tron-Ymomet

Bibliographic record

VenueConvergences francophones · 2019
Typearticle
Languagefr
FieldMedicine
TopicHistorical and Scientific Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

L’arrivée de 1900 terrifie les consciences françaises. Le sentiment de déréliction véhiculé par cette apocalypse s’incarne de manière privilégiée dans l’organe génital féminin. Il est celui qui a permis la première transgression : sa mise à nu sécrète les éléments d’une épiphanie redoutable. La littérature et la clinique fin-de-siècle captent cette inclination et mettent en scène les multiples transgressions féminines : transgressions psychiques (folles et hystériques), corporelles (avortées et femmes châtrées), pathologiques (syphilitiques et nymphomanes) ou encore sexuelles (onanistes et saphistes) balayent toutes les productions écrites de l’époque. L’objectif de cette communication serait ainsi d’étudier la représentation de ces transgressions du sexe féminin dans les textes romanesques, du naturalisme au décadentisme, mais aussi des textes issus de la clinique, thèses et articles de médecine, montrant par là une complémentarité des écritures. A cette question s’ajoute celle de la finalité : pourquoi exposer le mal ? Faire le choix d’une telle thématique n’est-ce pas proprement provocateur ? Ne traduit-il pas, in fine, une volonté du romancier et du médecin de s’affirmer soi-même comme transgressif ? Dans quelle mesure cette exposition ne correspondrait-elle pas à un exercice cathartique ? Nous analyserons cet espace de tension organique et littéraire posée par la matrice fin-de-siècle.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0220.004

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.

Opus teacher head0.029
GPT teacher head0.281
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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