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Avec John Dewey, penser l’expérience du bonheur au cœur du travail éducatif

2023· article· fr· W4379515784 on OpenAlexaff
Christophe Point

Bibliographic record

VenueÉduquer · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicEducational Philosophies and Pedagogies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesPhilosophySociology

Abstract

fetched live from OpenAlex

Ce travail a pour objectif de cerner la place que la notion de bonheur peut prendre au sein de la philosophie de l’éducation de John Dewey. Ce philosophe pragmatiste et pédagogue américain est l’auteur d’une philosophie de l’éducation originale. Nous explorons cette dernière à la recherche d’éléments théoriques permettant de redéfinir le bonheur comme qualité particulière d’une expérience significative et porteuse de sens avec d’autres expériences passées et à venir. Expérience subjective, à la fois sensible et intellectuelle, processuelle et sociale, le bonheur ainsi reconstruit théoriquement n’est pas étranger au travail éducatif. Au contraire, notre travail explore les relations entre l’expérience du bonheur et la relation pédagogique entre l’élève et l’enseignant. Comment penser le bonheur pour voir en lui l’indice d’un processus d’apprentissage réussi ? Nous concluons ainsi notre travail par quelques propositions pour imaginer les implications pragmatistes de l’expérience du bonheur pour le travail pédagogique des enseignants, leurs éthiques professionnelles et l’organisation des écoles.

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)0.003
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0150.022
Scholarly communication0.0090.013
Open science0.0010.007
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0140.002

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 head0.056
GPT teacher head0.328
Teacher spread0.272 · 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
Published2023
Admission routes1
Has abstractyes

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