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Record W2797504237 · doi:10.7202/1043753ar

ADAPTER EN CLASSE DES EXTRAITS DE ROMAN SOUS FORME DE BD : QUELS APPORTS POUR LA LECTURE LITTÉRAIRE ?

2017· article· fr· W2797504237 on OpenAlexvenueno aff
Nicolas Rouvière

Bibliographic record

VenueRevue de recherches en littératie médiatique multimodale · 2017
Typearticle
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAdapter (computing)ArtHumanitiesMedicineComputer scienceOperating system

Abstract

fetched live from OpenAlex

Cet article présente l’adaptation d’extraits de roman en planches de BD par les élèves d’une classe de lycée général. De la formulation collective d’une intention de sens, en amont de la production, jusqu’au commentaire explicatif des réalisations en aval, l’article examine comment le détour hétéro-sémiotique par un autre médium peut rejaillir sur la lecture du texte littéraire. Si l’activité fictionnalisante du lecteur est indéniablement mise en mouvement dans toutes ses dimensions, elle ne trouve pas véritablement son pendant objectivant dans un retour au texte. C’est la dimension multimodale et le passage par la bibliothèque d’images, plus que la dimension transmodale, qui s’avère efficace pour dépasser la compréhension littérale. En revanche, le processus d’adaptation, en redéfinissant la frontière entre lecteur et auteur, favorise chez les élèves un recul réflexif sur les stratégies narratives, dans la production de la planche de BD elle-même.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.666
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.326
Teacher spread0.255 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

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