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Record W3006344706 · doi:10.7202/1066847ar

Ce que les romans d’amour ont à nous apprendre

2020· article· fr· W3006344706 on OpenAlexaffvenue
Anne-Marie Shink

Bibliographic record

VenueCahiers d histoire · 2020
Typearticle
Languagefr
FieldArts and Humanities
TopicLiterature and Culture Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Souvent méprisés par les critiques ou relégués à la catégorie « littérature de plage », les romans d’amour ont pourtant beaucoup à nous apprendre sur la façon dont le sentiment amoureux a évolué au fil des siècles. Que s’est-il produit pour que l’on passe de l’amour héroïque de l’époque médiévale à l’amour fluide de l’époque moderne ? Dans cette communication, nous verrons comment la définition de l’amour s’est transformée pour s’adapter aux réalités et aux exigences de la société occidentale. Nous verrons aussi le rôle prédominant de l’époque victorienne dans l’établissement d’une définition de l’amour, mais aussi du flirt, du mariage et de la félicité conjugale. Ceci nous amènera à examiner comment l’amour est appréhendé aujourd’hui et comment nous négocions l’écart entre la vision victorienne de l’amour et les impératifs du XXI e 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.535
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.013
GPT teacher head0.185
Teacher spread0.172 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes2
Has abstractyes

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