Bibliographic record
Abstract
Dans cet article, je propose d’analyser le concept de bonheur en termes d’humeurs positives. Je montre que cette analyse constitue une voie moyenne entre l’analyse du bonheur comme émotion et l’analyse du bonheur en termes de propensions émotionnelles. Je soutiens plus particulièrement qu’être heureux consiste à ressentir une humeur positive, quelle que soit cette humeur. Cette analyse est donc réductionniste – être heureux n’est rien d’autre qu’être de bonne humeur – et pluraliste –nos bonnes humeurs, dans toute leur diversité, constituent autant de manières différentes d’être heureux. Afin de soutenir cette approche, je défends la thèse selon laquelle les humeurs sont des attitudes prospectives, consistant à évaluer l’impact affectif possible de notre environnement sur nos états motivationnels tels que nos désirs ou nos préférences. Je m’efforce ensuite d’explorer les relations que le bonheur entretient avec le bien-être à partir de cette analyse du bonheur comme bonne humeur.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".