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Record W4312399909 · doi:10.57086/dfles.103

Du Français Intensif à l’approche neurolinguistique : adaptations et défis en Chine

2020· article· fr· W4312399909 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDidactique du FLES · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Parmi les premiers terrains expérimentaux à l’approche neurolinguistique en dehors du Canada, la Chine offrait de nouvelles possibilités à l’implantation de l’approche pour un public adulte universitaire. Malgré des contraintes institutionnelles fortes et une culture d’apprentissage différente, l’implantation et l’adaptation de l’ANL sont un succès puisqu’elle confère des habiletés à communiquer solides à l’oral comme à l’écrit et une forte confiance en soi. Cependant, les difficultés rencontrées ont concerné la préparation à l’examen national, le TFS4, et l’adaptation des unités canadiennes pour un public universitaire qui doit rapidement acquérir des compétences linguistiques et lexicales ainsi qu’une méthodologie d’apprentissage exigeante.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.310
Teacher spread0.282 · 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