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Record W4376142447 · doi:10.4000/trema.8090

Enseigner la littérature numérique au secondaire, entre innovation et sédimentation : analyse de cas autour d’une recherche collaborative

2023· article· fr· W4376142447 on OpenAlex
Magali Brunel, Eleonora Acerra, Nathalie Lacelle

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTréma · 2023
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

L’article vise à montrer comment quatre enseignantes du secondaire français et québécois ont fait évoluer leurs pratiques, découvrant et enseignant un objet nouveau pour elles, la littérature numérique. Deux objectifs sont notamment poursuivis. D’une part, l’analyse se propose de décrire comment les enseignantes se sont appropriées ce nouvel objet d’enseignement à partir de savoirs existants et nouveaux ainsi que d’expériences lectorales personnelles. Seront notamment observés la prise en compte des spécificités technolittéraires de l’œuvre numérique et les choix ayant entrainé des évolutions — innovations et recompositions — dans leurs planifications et pratiques pédagogiques. D’autre part, l’article étudie comment le dispositif de recherche — lui-même innovant — a pu favoriser les déplacements identifiés dans les pratiques et réduire le sentiment d’insécurité qui est souvent associé au changement (Marsollier, 1999).

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.010
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.260
GPT teacher head0.385
Teacher spread0.124 · 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