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Les cultures spécifiques aux disciplines à enseigner à l’école secondaire, un objet fécond pour la recherche et la formation ?

2019· article· fr· W3110693411 on OpenAlexaff
Frédéric Saussez

Bibliographic record

VenueRecherche & formation · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDisciplineSociologyPedagogyMathematics educationPsychologySocial science

Abstract

fetched live from OpenAlex

Cette contribution discute l’hypothèse selon laquelle les disciplines à enseigner constituent un organisateur central du travail enseignant et de l’expérience des enseignantes de l’école secondaire ainsi que le vecteur du développement de cultures disciplinaires particulières. Elle a pour but de discuter de la fécondité de cette notion pour comprendre et intervenir sur les processus d’apprentissage de l’enseignement et de développement professionnel des enseignantes de l’école secondaire. À partir d’un survol du contexte de recherche anglo-saxon dans lequel cette notion est apparue et de l’illustration de la façon dont elle a migré vers la recherche sur l’apprentissage de l’enseignement, cette contribution soulève différentes questions relatives à la fécondité et aux limites de cette notion en lien avec le processus de socialisation des personnes à ces cultures et la façon dont ces cultures se matérialisent dans des façons de construire du sens autour de l’expérience laborieuse et de faire la classe.

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)0.012
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.022
Scholarly communication0.0150.010
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.558
GPT teacher head0.509
Teacher spread0.050 · 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 designTheoretical or conceptual
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

Citations3
Published2019
Admission routes1
Has abstractyes

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