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Record W2968682945 · doi:10.26220/rev.3088

Des enseignants de Sciences en immersion dans un musée. Une étude de cas sous l’angle de la théorie sociale de l’apprentissage

2019· article· fr· W2968682945 on OpenAlexaffabout
Anik Meunier, Charlène Bélanger, Patrick Charland

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSociologyMeaning (existential)HumanitiesPedagogyPsychologyArt

Abstract

fetched live from OpenAlex

Le présent projet s’est intéressé à une formation continue proposée à des enseignants de sciences au secondaire dans un musée de société québécois. Nous avons suivi ce groupe d’enseignants tout au long du processus d’immersion dans les activités du musée, pour décrire et tenter de comprendre la manière dont cette expérience a transformé leur pratique d’enseignement, ainsi que leur relation au musée. Une recherche qualitative-interprétative privilégiant une approche inductive a été mise de l’avant par une étude de cas sous l’angle de la théorie sociale de l’apprentissage Wenger (1998, 2009). L’apprentissage y est décrit comme un système à quatre dimensions étroitement liées et se déterminant mutuellement. Les résultats obtenus rendent compte de ces quatre dimensions : la signification, l’engagement dans la pratique, l’appartenance à une communauté et les changements sur le plan de l’identité et expliquent comment la participation aux activités collectives transforme les individus.

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)0.007
metaresearch head score (Gemma)0.011
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0160.025
Scholarly communication0.0100.008
Open science0.0010.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.026
GPT teacher head0.246
Teacher spread0.221 · 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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

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Same topicMuseums and Cultural HeritageFrench-language works237,207