Bibliographic record
Abstract
L’émotion est abordée non pas sous une perspective individuelle et causale, mais comme un mode de structuration et de développement de l’interaction ; l’unité d’étude est l’épisode émotionnel. On distingue ensuite grandes émotions et micro-émotions, qui naissent et disparaissent dans le flux de l’interaction, comme on le montrera sur une étude de cas avec l’interjection “ah merde”. La conclusion porte sur l’apprentissage de l’émotion comme acquisition d’une compétence langagière et interactionnelle. Abstract : Micro-emotions in interactions : “oh, shit, there’s nothing for mum”. Emotions are approached not as individual, causally determined phenomena, but as specific ways of developping social interactions. The emotion episode is defined. “Big” emotions are distinguished from “micro”-emotions, which appear and disappear in the interaction. The French interjection “ah merde!” [“oh shit!”] will be taken as an example of such micro-emotion. The concluding remarks refer to emotion education as the acquisition of a linguistic and interactional capacity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".