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Record W4313317055 · doi:10.4000/alsic.6319

Concevoir des parcours immersifs en français langue seconde pour préparer les étudiants étrangers à la mobilité

2022· article· fr· W4313317055 on OpenAlexaff
Nicolas Guichon, Julien Thiburce, Justine Lascar, Sofiane Doulfaquar

Bibliographic record

VenueAlsic · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicEducational Tools and Methods
Canadian institutionsUniversité du QuébecUniversité du Québec à Montréal
FundersAgence Nationale de la Recherche
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Avec la place grandissante des apprentissages médiés par les technologies, aussi bien dans le milieu académique que pour le grand public, il semble opportun d'évaluer les possibilités offertes par les technologies immersives pour fournir des expériences alternatives d'apprentissage d'une langue-culture. Le projet Visiteurs s'appuie sur la conception d'un parcours immersif qui vise à accompagner l'exploration de l'espace public par des étudiants étrangers. Certaines étapes de la conception de ce parcours pilote fournissent un espace privilégié pour examiner l'apport des technologies immersives pour un apprentissage situé d'une langue seconde et pour générer des méthodes et des concepts propices à nourrir la recherche en ingénierie didactique.

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)0.009
metaresearch head score (Gemma)0.014
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: none
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.005
Scholarly communication0.0080.006
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.002

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 head0.050
GPT teacher head0.363
Teacher spread0.312 · 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".

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Citations7
Published2022
Admission routes1
Has abstractyes

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