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Record W2887944319 · doi:10.7202/1050942ar

ÉDITER LA CROISADE DES ENFANTS DE MARCEL SCHWOB EN VERSION ENRICHIE : QUELS ENJEUX DE RÉCEPTION ?

2018· article· fr· W2887944319 on OpenAlexvenueno aff
Marie-Armelle Camussi-Ni, Catherine Daniel, Solenn Dupas, Nathalie Brillant-Rannou

Bibliographic record

VenueRevue de recherches en littératie médiatique multimodale · 2018
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

L’article qui suit est issu d’un entretien entre Nathalie Brillant-Rannou et des coordinatrices de l’édition enrichie de La Croisade des enfants de Marcel Schwob. Le groupe « Lectures et médiations numériques » (EA CELLAM, Université Rennes 2) a engagé une recherche-action reposant sur la conception d’un livre au format ePub3 à partir de ce récit du XIXe siècle, en collaboration avec le studio L’Apprimerie. Cet ouvrage enrichi est appelé à faire l’objet d’une étude de réception dans un second volet du projet. À travers cette démarche, il s’agit de voir dans quelle mesure la remédiatisation d’une oeuvre littéraire est à même d’en favoriser l’appropriation en décloisonnant les horizons d’attente et les habitudes de lecture. Les contenus émergents que constituent les livres numériques enrichis invitent en effet à dépasser les oppositions binaires écran-papier et lecture linéaire-lecture délinéarisée. Les oeuvres patrimoniales remédiatisées qui intègrent de l’hypertexte et des contenus multimédias ouvrent des pistes de réflexion spécifiques au sein de cet ensemble. Cet entretien porte sur les choix éditoriaux effectués et sur les hypothèses de réception qui les ont sous-tendus, particulièrement en termes de compétences de lecture et d’enjeux de patrimonialisation.

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)0.002
metaresearch head score (Gemma)0.012
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0180.006

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 head0.209
GPT teacher head0.366
Teacher spread0.158 · 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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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