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Record W4285555378 · doi:10.7202/1089656ar

Griffonner, gribouiller, déchirer l’album numérique ?

2021· article· fr· W4285555378 on OpenAlexvenueno aff
Euriell Gobbé-Mévellec

Bibliographic record

VenueSens public · 2021
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

L’étude qui suit interroge la matérialité de l’album pour la jeunesse, en s’intéressant aussi bien aux formes imprimées qu’aux formes numériques – et aux relations qu’elles entretiennent entre elles – à l’époque contemporaine. Elle propose, en s'appuyant sur certains travaux de Ségolène Le Men, de (re)définir l'album comme un support capable de prendre en compte les modalités profanatrices d'appropriation du livre par un lectorat qui ignore encore les codes, le rituel, le protocole de la lecture (un lectorat qui secoue, déchire, gribouille, tient le livre à l'envers, commence par la fin, etc.). À la question de savoir si le livre numérique pour enfant, fondé sur la lecture tactile et sur une interactivité inhérente au support, constitue un relai numérique à cette dynamique de la littérature de jeunesse, c’est-à-dire, si l’on peut envisager l’émergence d’un album numérique pour la jeunesse, l’étude conclut sur un relatif échec, pour l’instant, de la littérature de jeunesse numérique à développer une forme d’indicialité authentique, capable d’accueillir le geste profanateur de l’enfant.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0070.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.253
GPT teacher head0.298
Teacher spread0.046 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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