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Record W3175639298 · doi:10.4000/belphegor.3948

La transition numérique de la bande dessinée franco-belge, une mutation impossible ?

2021· article· fr· W3175639298 on OpenAlexvenueno aff
Raphaël Baroni, Gaëlle Kovaliv, Olivier Stucky

Bibliographic record

VenueBelphégor · 2021
Typearticle
Languagefr
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Cet article propose d’interroger la transition numérique de la bande dessinée franco-belge. Alors que d’autres médias ont été fortement reconfigurés par le numérique, nous montrerons que la bande dessinée semble opposer une résistance. L’étude historique des supports de publication permettra, dans un premier temps, d’éclairer les processus de reconfiguration liés à la multiplicité des supports de publication analogiques et numériques. Cette partie historique soulignera l’importance symbolique du rattachement de la bande dessinée au support du livre et les obstacles qui s’opposent à son transfert vers les écrans. Une seconde partie, fondée sur une analyse des représentations des acteurs, montrera comment les technologies numériques ont transformé le marché, les pratiques et l’identité professionnelle des auteurs et des autrices. Nous verrons à quel point le livre demeure une source de revenus difficilement remplaçable et une référence symbolique majeure pour un média dont le patrimoine ne se laisse pas facilement transférer sur des interfaces numériques. En l’absence de véritables alternatives économiques et du fait d’une émancipation médiatique des auteurs contemporains les plus créatifs, les déclinaisons numériques de la bande dessinée ne parviennent, ainsi, pas à transformer en profondeur le champ de production.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.589

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.006
Scholarly communication0.0070.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.001

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.

Opus teacher head0.013
GPT teacher head0.253
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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