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Record W2954370220 · doi:10.7202/1060847ar

Voix d’élèves en difficulté dans un dispositif d’entretien télévisé

2019· article· fr· W2954370220 on OpenAlexaffvenueabout
Tommy Collin-Vallée, Geneviève Fortier-Moreau, Maryvonne Merri

Bibliographic record

VenueÉducation et francophonie · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Les travaux anglo-saxons sur la Student voice (Cook-Sather, 2002; Fielding, 2007) se préoccupent des conditions permettant aux élèves d’exprimer leurs expériences sur l’apprentissage, l’enseignement et la scolarité. Ils ont laissé en suspens la question de la voix des élèves à risque de décrochage scolaire dans des dispositifs extra-académiques. Nous proposons d’interroger ici la possibilité d’une prise de parole des élèves dans des dispositifs d’entretiens télévisés entre un élève, ses parents et une orthopédagogue. Ces entretiens sont extraits du docu-feuilleton québécois Les persévérants (Ferron et Baer, 2014) qui met en oeuvre un programme de prévention du décrochage scolaire. Notre démarche d’analyse des discours en interaction selon une approche structurelle et opératoire s’appuie sur le cadre de Davis (1986). Cette démarche se décline en deux étapes par la description : a) des thèmes et tâches accomplies dans chaque entretien et b) des opérations et procédés mis en oeuvre par l’orthopédagogue. Les résultats mettent en évidence que la parole des élèves permet la confirmation d’un problème préconçu par l’orthopédagogue et une légitimation de son expertise.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.006
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0160.002

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 head0.012
GPT teacher head0.280
Teacher spread0.268 · 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

Citations2
Published2019
Admission routes3
Has abstractyes

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