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Record W4416042201 · doi:10.5206/cie-eci.v54i2.20179

In Their Own Words: Student Narratives of Intercultural Engagement, Barriers, and Impact at a Canadian University

2025· article· en· W4416042201 on OpenAlexaffvenueabout
Yujie Jiang, Kyra Garson, Amie McLean, Alana Hoare, Anila Virani, Brad Harasymchuk

Bibliographic record

VenueComparative and International Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsTransformative learningIntercultural learningContext (archaeology)InternationalizationIntercultural relationsCultural diversityIntercultural communicationInclusion (mineral)Diversity (politics)

Abstract

fetched live from OpenAlex

Enhancing and evaluating intercultural learning in Canadian postsecondary education is increasingly important in the context of domestic student diversity, the rapid internationalization of higher education, and collective responsibilities to address the local and global implications of colonialism at relational, institutional, and systemic levels. This paper provides the findings of qualitative research designed to explore student perspectives on their intercultural attitudes, knowledges, and skills, as well as to identify whether and how their learning resulted in intercultural praxis. Data from a student survey coded and thematically analyzed revealed a variety of themes of interest to educators. The findings highlight the need to address individual and institutional barriers to intercultural learning, while acknowledging that societal barriers also constrict the advancement of inclusion and diversity goals. Key areas for improvement include moving beyond deficit models of cultural diversity; enhancing pedagogical supports and professional development in intercultural praxis; and progressing past superficial or individualistic approaches in ways that empower students to apply their intercultural knowledges and skills through transformative actions that gesture towards socially just futures.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.059
GPT teacher head0.412
Teacher spread0.353 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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