« Où sont les super-héros français ? Du <i>Comte de Monte-Cristo</i> à <i>Astérix</i> en passant par <i>Mikros & Photonik</i> »
Bibliographic record
Abstract
Superman, Batman, Wonder Woman, The Fantastic 4, Spider-Man, X-Men, Black Panther (1966, and its filmic adaptation), all the superheroes are American. All? No, there are some since 1938 in France as original French creations and as the translations of the American Superheroes. But who heard about Fantax, Santanax, Misterx? Except for some thousands fans, these x illustrate, even if unwittingly, unknown heroes. The only French known and famous super-heroes in France, and a bit beyond its borders, are Asté rix and Superdupont, that is, parodies of superheroes. In this article, I explain why French superheroes never succeeded in adapting within the Franco-European comics world. I. There are many factors that would explain that absence and they are also intertwined: from the most obvious such as the censorship through the July-1949-Law to protect youth, to factors much less talked about like the absence of BD creators of Jewish origin, the negative connotations of the word ‘superman’ (ubermensch), explicit and tacit anti-americanism, the decline of the French power including in the cultural subfield (cultural geopolitics) and the French cultural policies that were, for too long, elitist, and included the disregard of BD and comics and even more of the genre superhero.…
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".