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Record W7117878693 · doi:10.53967/cje-rce.7589

(Book Review) Embodying Intercultural Capacities: The Pedagogic Impact of Study Abroad

2025· article· en· W7117878693 on OpenAlexaffvenue
Yifan Liu

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStudy abroadQualitative researchHigher educationAgency (philosophy)Context (archaeology)Ethnography

Abstract

fetched live from OpenAlex

Study abroad is often treated as a transformative journey that will automatically give students a new way of seeing the world.Embodying Intercultural Capacities: The Pedagogic Impact of Study Abroad by Kate Naidu asks some critical questions: does studying abroad really help students develop intercultural capacities?If so, how, and under what conditions, do such capacities actually take shape?Drawing on a longitudinal study with Australian students in Indonesia, Naidu (2025) develops the term "interculturalization" (p. 5) to understand the slow and uneven process through which students learn to work on themselves as they navigate cultural differences.She argues that intercultural capacities are not a taken-for-granted by-product of immersion in a host country but are developed through a cumulative pedagogic process that requires active labour of both teaching and learning.This shift offers a critical lens for understanding student mobility, moving beyond what students learn to how their capacities gradually take form.The book is situated within the context of the Australian Consortium for In-Country Indonesian Studies (ACICIS), focusing on Australian university students undertaking semester-long programs in Yogyakarta, Indonesia.This setting represents a trajectory that is much less addressed in the literature on international student mobility where students from developed countries pursue studies in developing countries, including in Asia.After setting out the research landscape in Chapter 1, Chapter 2 addresses the methodological challenge of "capturing the intercultural" (p.21).Naidu tracked 16 undergra-Compte rendu: Liu xvi

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)0.001
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.060
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0600.017

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 head0.401
Teacher spread0.342 · 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
GenreReview

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 routes2
Has abstractyes

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