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Record W220724439 · doi:10.7202/1047795ar

UN DISPOSITIF D’APPROCHE MULTIMODALE DE LA LECTURE LITTÉRAIRE

2018· article· fr· W220724439 on OpenAlexvenueno aff
Hélène Cuin

Bibliographic record

VenueRevue de recherches en littératie médiatique multimodale · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicHistorical and Political Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

Notre communication concerne la lecture littéraire en classe de seconde au lycée et s’ancre dans le constat d’un rapport problématique des élèves à la lecture littéraire et aux oeuvres patrimoniales. Dans le cadre d’une recherche exploratoire, nous avons fait le choix d’associer et de conjuguer l’actualisation (Citton, 2007) des textes selon une approche multimodale et des activités régulières mettant en jeu le sujet lecteur et avons fait aboutir notre démarche sur la production d’un texte de lecteur (Mazauric, Fourtanier et Langlade, 2011) multimodal. Dans le cadre d’un enseignement ordinaire (une séquence de lecture en seconde consacrée à l’étude de Tartuffe en oeuvre intégrale), comment la production d’une écriture de commentaire sous forme multimodale est-elle susceptible de manifester certaines compétences de lecture littéraire ? Dans cette perspective, nous avons convoqué la notion de lecture littéraire (Dufays, Gemenne et Ledur, 2009) et avons émis l’hypothèse que l’introduction à l’école des pratiques sociales de référence des élèves dominées par la multimodalité (Donnat, 2008 ; Lebrun, Lacelle et Boutin, 2012) serait susceptible de favoriser le rapport à la lecture littéraire et aux oeuvres patrimoniales. Les premiers résultats semblent confirmer cette hypothèse et ouvrent des pistes sur la question du sujet de lecture spectature (Lacelle, 2009).

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)0.006
metaresearch head score (Gemma)0.015
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 score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0090.009
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0440.012

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 head0.041
GPT teacher head0.327
Teacher spread0.287 · 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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Citations0
Published2018
Admission routes1
Has abstractyes

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