Bibliographic record
Abstract
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 XXIe 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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".