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Record W2560168363 · doi:10.18192/olbiwp.v6i0.1129

Radiographie de l’immersion dans l’enseignement supérieur en Suisse et à l’Université de Fribourg : les pré-requis nécessaires

2015· article· fr· W2560168363 on OpenAlexvenueno aff
Aline Gohard-Radenkovic

Bibliographic record

VenueOLBI Journal · 2015
Typearticle
Languagefr
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

De nos jours, la notion d’immersion est le plus souvent liée à celle d’éducation bilingue, proposant des dispositifs qui réinventent en quelque sorte l’appropriation naturelle d’une langue étrangère ou seconde dans le cadre de politiques linguistiques officielles. Toutefois l’immersion naturelle en langues existe depuis le Moyen-Âge en Europe, notamment en Suisse, surtout dans les régions frontalières. Que s’est-il passé entre-temps pour que le dispositif de l’immersion soit devenu l’atout majeur au service de cette éducation bilingue ? Après avoir brièvement rappelé les différentes conceptions et réalisations de l’immersion, nous examinerons les dispositifs existants de l’immersion dans l’enseignement supérieur en Suisse. Nous nous arrêterons à l’Université officiellement bilingue de Fribourg (français–allemand) où nous analyserons les offres d’immersion, plus spécifiquement le programme Bilingue Plus. Enfin nous nous demanderons quels sont les pré-requis de cette éducation au bi/plurilinguisme à l’heure où les apprenants pourraient simplement se contenter de l’apprentissage d’une seule langue, l’anglais.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.256
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.005
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.002

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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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