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Record W1567188846 · doi:10.7202/1047807ar

DÉVELOPPER CONJOINTEMENT LA COMPÉTENCE MULTIMODALE ET LES COMPÉTENCES SÉMIOTIQUES SPÉCIFIQUES À L’AIDE D’UN DISPOSITIF ASSOCIANT LA LITTÉRATURE, LES SÉRIES TÉLÉVISÉES ET LA BANDE DESSINÉE

2018· article· fr· W1567188846 on OpenAlexvenueno aff
David Vrydaghs

Bibliographic record

VenueRevue de recherches en littératie médiatique multimodale · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Le développement d’une littératie multimodale s’est accompagné de l’essor de réflexions didactiques soulignant l’importance de la compétence multimodale comme compétence capable d’améliorer la lecture littéraire et les compétences sémiotiques propres à chaque média abordé. On cherchera à montrer l’intérêt d’une telle combinaison à partir d’un dispositif associant des multitextes en apparence très différents (soit une oeuvre littéraire, une bande dessinée et un extrait de série télévisée consacrés à des personnages, des histoires et des univers distincts). Celui-ci a été élaboré dans le cadre d’une activité de formation continuée d’enseignants de français du secondaire (Belgique). Il a été testé et co-construit par les enseignants. Les résultats de cette expérimentation tendent à confirmer l’importance de la compétence multimodale pour le développement de compétences propres à la lecture littéraire.

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)0.004
metaresearch head score (Gemma)0.010
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.082
GPT teacher head0.373
Teacher spread0.291 · 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
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

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueRevue de recherches en littératie médiatique multimodaleSame topicFrench Language Learning MethodsFrench-language works237,207