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Record W7011230997

Le retour sur le tandem en tant que méthode d’(auto)évaluation en langue étrangère

2016· article· fr· W7011230997 on OpenAlexfundno aff

Bibliographic record

VenueApplied Medical Informatics (University of Medicine and Pharmacy Cluj-Napoca) · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsLinguistic analysisField (mathematics)Translation studiesLexicology
DOInot available

Abstract

fetched live from OpenAlex

Vu comme une forme d'évaluation, le retour sur le tandem s'avère être une activité complexe puisqu'il implique non seulement l'évaluation des étudiants par l'enseignant, mais à la fois l'évaluation réciproque des participants au tandem.L'information fournie par les étudiants, de même que la manière dont elle est modulée, permet une analyse poussée du tandem en tant que processus d'apprentissage.Dans cette perspective, notre recherche se penche sur les interviews de retour réalisées avec 8 tandems franco-roumains.Elle comporte deux volets.Le volet quantitatif est représenté par le répertoire des thèmes récurrents choisis par les tandems et des similitudes dans le recours à la langue maternelle et à la langue étrangère durant l'interview avec l'enseignant.À cela s'ajoute une réflexion sur la zone de confort linguistique telle qu'elle se présente dans les interviews de retour sur le tandem.Le volet qualitatif prend en compte la modulation de l'information par les apprenants et les modalités de réalisation de l'interaction entre les partenaires du tandem, en présence de l'enseignant évaluateur.

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)0.036
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.036
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.007
Scholarly communication0.0090.009
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.003

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 head0.059
GPT teacher head0.320
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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