Le manga est-il nécessairement lié à un contexte de production ?
Bibliographic record
Abstract
Le manga est-il nécessairement lié à un contexte de production ? Si ce format s’avère indissociable de son origine, alors il ne peut s’exporter et se réimplanter en France. Nous développons cette thèse en quatre étapes : l’analyse du manga Bakuman comme journal interne de la compagnie Shūeisha, d’après la théorie d’Ikujiro Nonaka, puis la manière dont l’histoire du Japon a façonné celle du manga. Ensuite, nous montrons que le manga en tant qu’objet est né de nécessités techniques et économiques. Enfin en France, les difficultés rencontrées n’établissent pas en droit mais seulement en fait une impossible transposition des modèles — notamment en raison des différentes conceptions de l’art — comme en témoignent certains succès français. Is manga necessarily determined by its production context? If this kind is inseparable from its origin, then it cannot be exported and reimplanted in France. We develop this thesis in four steps : analysis of the manga Bakuman as internal newsletter of the Shūeisha company, according to the theory of Ikujiro Nonaka, then how Japanese history has shaped manga. Then we show that manga was born as an object of technical and economic requirements. Finally in France, difficulties doesn’t prove an impossible transposition of models — especially the different conceptions of art — because of some french success.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".