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Record W4402546881 · doi:10.1177/10283153241275035

“Intercultural Encounters”: Mentorship Relations as Spaces for Critical Intercultural Learning in Higher Education Institutions (HEIs)

2024· article· en· W4402546881 on OpenAlexaffabout
Christina Sachpasidi, Barbara Bompani, Cynthia Nicol

Bibliographic record

VenueJournal of Studies in International Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntercultural learningHigher educationMentorshipIntercultural relationsIntercultural communicationPedagogyIntercultural competenceTransformative learningSociologyPolitical science

Abstract

fetched live from OpenAlex

There are growing numbers of African international students studying at Higher Education Institutions (HEIs) in North America and the United Kingdom. Intercultural mentoring is one response to supporting students in navigating the complex cultural, social, and academic transitions from home to host countries. This article examines the experiences of 18 participants who had recently mentored African international students attending higher education institutions in Canada or in the UK. Semi-structured interviews with participating mentors were transcribed and analysed from a critical intercultural perspective. Results highlight four themes that provide insight into mentors’ approaches to intercultural mentoring: navigating fields of action and intervention, engaging in reflective practice, intercultural mentoring as a relational practice, and mentoring as a decolonising practice. Study findings provide insight into how intercultural mentoring relationships develop and evolve and how mentors approach mentoring relationships as sites that hold transformative learning potential for both mentors and students.

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)0.012
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0190.021
Scholarly communication0.0130.007
Open science0.0020.019
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.001

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 head0.175
GPT teacher head0.504
Teacher spread0.330 · 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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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