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Record W4409282029 · doi:10.18192/olbij.v14i1.7041

Critical Framing of Transversal Competences: Promoting Intercultural Responsibility through Cross-language and Cross-curricular Teacher Collaborations

2025· article· en· W4409282029 on OpenAlexaffvenueabout
Sunny Man Chu Lau

Bibliographic record

VenueOLBI Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsBishop's University
Fundersnot available
KeywordsFraming (construction)PedagogyCross-culturalCommunicative competencePolitical scienceIntercultural competenceSociologyPsychologyEngineering

Abstract

fetched live from OpenAlex

Transversal competences have gained importance in educational programs, particularly with the emphasis on plurilingual approaches in the Common European Framework of Reference. These competences include global citizenship, intercultural communication, and critical thinking. However, many educational statements define these competences narrowly, often reflecting a neoliberal agenda focused on market-oriented education. This paper repositions transversal competences within critical pedagogy and decolonial perspectives, centering language education on students’ critical global perspectives and intercultural reflexivity for civic engagement. Drawing on Guilherme’s concept of intercultural responsibility, I discuss a collaborative action research study with two Quebec elementary teachers (English and French) to show how their cross-language efforts promoted students’ transferable language skills, enhanced students’ cultural awareness, and fostered a reflexive disposition to work across differences and embrace collective responsibility in an increasingly interconnected world.

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)0.022
metaresearch head score (Gemma)0.019
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0130.030
Scholarly communication0.0130.011
Open science0.0020.020
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.426
Teacher spread0.401 · 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
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
Published2025
Admission routes3
Has abstractyes

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