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Record W2109720458

DESIGNING A FRENCH FOR LIBERAL ARTS PROFESSIONS COURSE

2014· article· en· W2109720458 on OpenAlexaboutno aff
Mariana Ionescu

Bibliographic record

VenueJournal of International Scientific Publications : Language, Individual & Society/Language, individual and society · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusLiberal arts educationWork (physics)Course (navigation)Order (exchange)Computer scienceEngineering ethicsThe artsPedagogyMathematics educationMedical educationEngineeringHigher educationPsychologyPolitical scienceMedicineBusiness
DOInot available

Abstract

fetched live from OpenAlex

French for Specific Purposes (FSP) courses prove to be challenging for both learners and teachers because they have to be tailored to audiences with increasingly specialized needs who are concerned, to various degrees, about their integration in a French-speaking professional work environment. This paper will respond briefly to the main questions regarding the design and teaching of a French course for liberal arts professions which was created primarily for Canadian students interested in the legal, medical and educational fields, but is also open to students who need an extra language course in order to meet their program requirements. The designer of a second-language course that proposes specific training complementary to general linguistic training needs to carefully determine the course's learning objectives and teaching strategies, to create a syllabus which responds to multifunctional needs, and to choose evaluation techniques that are more suitable for learners who will eventually work in an intercultural environment.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.038
GPT teacher head0.373
Teacher spread0.336 · 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
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of International Scientific Publications : Language, Individual & Society/Language, individual and societySame topicFrench Language Learning MethodsFrench-language works237,207