Bibliographic record
Abstract
Si un auteur de vaudevilles comme Labiche se plaît à montrer des scènes de première rencontre amoureuse et s’il traite de la même façon une présentation officielle et la rencontre impromptue d’un inconnu, ce n’est pas par envie d’idéaliser le mariage d’amour et de rabaisser le mariage de raison, mais bien pour moquer l’un et l’autre. En se fondant sur une analyse interactionnelle des dialogues, de la structure de ceux-ci et du lien qu’ils instaurent entre les locuteurs, on observera que la rencontre amoureuse est un lieu privilégié de dérèglement de la conversation et, partant, de la mise en cause des codes et des conventions qui prévalent normalement au bon fonctionnement de la relation sociale. En renvoyant dos à dos le discours amoureux spontané et le discours amoureux contraint, Labiche s’amuse avec un motif traditionnel de la relation amoureuse.AbstractIf an author of “vaudevilles” like Labiche likes to show scenes of encounters, and if he deals the same way with an official introduction and an unexpected encounter with a stranger, it is not because he wants to idealize love matches and to belittle marriages of convenience but it is really to make fun of both of them. When we go by an interactional analysis of the dialogues, their structures and the link they establish between the speakers, we will thus notice that the love encounter is a privileged place for the unsettling of the conversation and, consequently, for the questioning of the codes and the conducts which usually prevail over the smooth running of social relations. By turning against each other the spontaneous love speech and the forced love speech, Labiche is playing with a traditional pattern of love relations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".