Bibliographic record
Abstract
Brentano et Husserl reconnurent tous deux que certaines de nos intentionnalités posent des enjeux normatifs. Seulement, Brentano croyait que les intentionnalités ne peuvent être qualifiées normativement que si elles sont incompatibles avec une autre façon opposée de se rapporter intentionnellement à un même objet. Husserl insista pour sa part sur le fait qu’il n’y a des enjeux normatifs que là où il y a une relation intentionnelle et positionnelle. Malgré cette distinction, en imposant ces conditions (incompatibilité et position) pour qu’il soit question de normativité intentionnelle, Brentano et Husserl se trouvèrent tous deux à favoriser un réductionnisme normatif. Je proposerai pour ma part, dans un geste rappelant celui de la pragmatique d’Austin mais sans m’y restreindre pour autant, de dépasser ce réductionnisme normatif en cessant de limiter à ces conditions l’application de normes à nos intentionnalités.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".