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Record W2966678463 · doi:10.7202/1062029ar

LES ENJEUX ASSOCIÉS À L’APPROPRIATION DE RESSOURCES NUMÉRIQUES MUSÉALES PAR DES ENSEIGNANTS DU SECONDAIRE DU QUÉBEC : PROPOSITION D’UN CADRE D’ANALYSE

2019· article· fr· W2966678463 on OpenAlexaffvenueabout
Katryne Ouellet, Marie-Claude Larouche, Denis Simard, Luc Prud’homme

Bibliographic record

VenueRevue de recherches en littératie médiatique multimodale · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsUniversité LavalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPolitical scienceHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

Depuis 2014, le Musée des beaux-arts de Montréal développe une plateforme nommée ÉducArt, qui suggère des activités éducatives réalisées à partir de sa collection encyclopédique pour soutenir les écoles secondaires québécoises dans leur mission culturelle. Comme l’élaboration de ressources destinées au milieu scolaire demande de tenir compte des besoins et des réalités de la classe (Van der Maren, 1996, 2003) et que le gouvernement du Québec entend poursuivre le financement de plateformes muséales (Rocheleau, 2016), il apparaît important d’étudier les enjeux associés à l’appropriation de ressources numériques muséales, telles que celles diffusées sur la plateforme ÉducArt, par des enseignants du secondaire du Québec. Dans cette perspective, un cadre d’analyse visant à mieux comprendre ces enjeux semble essentiel. Cet article se veut une contribution en ce sens, car à notre connaissance, aucun modèle théorique n’existe pour comprendre les enjeux particuliers que rencontrent des enseignants du secondaire lorsqu’ils tentent de s’approprier des ressources numériques muséales.

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 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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.316
Teacher spread0.250 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2019
Admission routes3
Has abstractyes

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