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Record W331564236

On-Site French Language and Quebec Culture Training for Business and Industry.

2000· article· en· W331564236 on OpenAlexaboutno aff
Sylvie Debevec Henning, Jonathan M. Slater

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsRelevance (law)CurriculumForeign languageFrenchClass (philosophy)Work (physics)PedagogySelection (genetic algorithm)SociologyComputer scienceLinguisticsPolitical scienceEngineeringArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

This paper documents the development and field testing of pedagogical materials needed for six 5-week modules of French language instruction and introduction to Quebec's Francophone culture and business practices. Presently available materials produced in the United States too often focus on France and French business practices. Similarly, materials produced in Canada are geared to non-French speaking Canadians. Materials described herein were adapted and developed to be more appropriate to the American adult English speaker interested in learning about Quebecois French and business practices. The materials are also suitable for on-site training sessions involving students at different levels of proficiency. This curriculum and modules were designed to be used in the northern part of New York state near the Quebec border. Reviewed are instructional design, including determining foreign language and culture needs, the make-up of the class, and selecting instructional materials and the format of instruction. Six modules cover three topics: getting acquainted, getting around, and getting down to business (over three modules). Other topics considered include the following: work relevance as criterion for content selection, awareness of managerial conditioning, instructional approach. (KFT) Reproductions supplied by EDRS are the best that can be made from the original document.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.336
Teacher spread0.308 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2000
Admission routes1
Has abstractyes

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