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The Common European Framework of Reference for Languages, the Intercultural Development Index, and Intercultural Communication Competence

2016· book-chapter· en· W2551073480 on OpenAlexaffabout
Karen Ragoonaden

Bibliographic record

VenueAdvances in higher education and professional development book series · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsIntercultural communicationIntercultural competencePedagogyCurriculumCommunicative competencePsychologyCompetence (human resources)CertificationMathematics educationPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Given the inherent pluralism of Canadian society, the emphasis on intercultural communication competence (ICC) is a logical extension of second language education in the 21st century. This chapter explores the import of implementing the Common European Framework of Reference for Languages (CEFR) in Teacher Education. To support the development of ICC, the Intercultural Development Inventory (IDI), a validated tool, was used to assess the intercultural communication competence of second language preservice teachers in Canada. The purpose of this discussion is to examine if teaching and learning about the CEFR in a Curriculum and Instruction course in the area of French as a second language can provide the necessary parameters to promote intercultural communication competence (ICC) of preservice language teachers. In order to assess ICC of preservice teachers, the Intercultural Development Index was administered during the Fall semester of a one year, Post-Baccalaureate Teacher Education Certification Program.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.037
GPT teacher head0.361
Teacher spread0.324 · 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 designTheoretical or conceptual
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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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Same venueAdvances in higher education and professional development book seriesSame topicInternational Student and Expatriate ChallengesFrench-language works237,207