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Record W4415677205 · doi:10.64899/871905beclxq

An Autoethnographic Study: Perspectives and Insights of a Cross-cultural Educator on Curriculum and Pedagogical Variation in Internationalization of Higher Education

2025· article· en· W4415677205 on OpenAlexaffabout
Ge Li

Bibliographic record

VenueJournal of Comparative & International Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCurriculumInternationalizationHigher educationNarrativeAutoethnographyInternational educationPerceptionConstructivist teaching methodsNarrative inquiry

Abstract

fetched live from OpenAlex

This auto-ethnographic inquiry utilizes autobiographical narrative to delve into my experiences and narratives as a cross-cultural educator simultaneously teaching at a Chinese and Canadian university. The aim is to ponder the interconnections, differences, and challenges within formal cross-cultural curriculum design and pedagogy, informal curriculum, and hidden curriculum and engage with academic environments and broader communities. Specifically, the study will examine the content, teaching methodologies, student learning, assessment practices, and the learning environment based on my experiences in an intercultural educational setting, as well as my coping strategies. The constructivist paradigm serves as the theoretical framework to explore my perceptions and understanding of these lived experiences. Data is drawn from my journals, memories, teaching materials, chats, emails, and memos written during my teaching experiences. Through my reflections, this study aims to reveal motivations, knowledge, and organizational discrepancies encountered in cross-cultural education as cross-cultural educators, proposing strategies to promote effective cross-cultural and cross-racial educational practices in the internationalization of higher education.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.094
GPT teacher head0.514
Teacher spread0.421 · 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 designObservational
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 routes2
Has abstractyes

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