Bibliographic record
Abstract
L'emploi réitéré de la notion de barbarie en lien avec les déchaînements de violence de ces dernières décennies justifie une étude de l'emploi de cette notion, ce qui permet de dégager en premier lieu, plusieurs figures de la barbarie, notamment autour des modes divers de violence. La lecture de trois auteurs, Jean-Pierre Dupuy, Jean-François Mattéi et Michel Henry permet ensuite de chercher l'origine de la violence, moins dans une haine de l'autre, différent, que dans une haine de soi et un besoin de reconnaissance. Il s'ensuit la nécessité de penser quelle réponse donner à la barbarie : La lecture de Paul Ricoeur, Axel Honneth et Marcel Hénaff suggère une recherche de piste dans la logique du don. Larticle se termine, en s'inspirant de certaines réflexions de Hannah Arendt, sur un questionnement quant à la visée de cette convocation de l'idée de barbarie.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".