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Record W4411148568 · doi:10.5206/cie-eci.v54i1.20951

Lessons Learned from a Near-Symmetrical North‒South Student Exchange/ International Service-Learning University Partnership

2025· article· en· W4411148568 on OpenAlexafffundvenueabout
Andrew Robinson, Robert Kwame, James Dzisah, Stacey Wilson-Forsberg

Bibliographic record

VenueComparative and International Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeneral partnershipService-learningService (business)Political scienceBusinessSociologyPedagogyMarketing

Abstract

fetched live from OpenAlex

This article reports on an interview-based assessment of the eight-year experience of a North‒South university partnership between a Canadian university and three universities in a Sub-Saharan African country. The partnership concerned an asymmetrical student exchange program whereby southern graduate students traveled north to study while northern undergraduates traveled south to perform international service-learning internships. Unlike conventional partnerships that tend to be hierarchical and one-way, the article finds this partnership demonstrated characteristics of Koehn and Obamba’s (2014) “near symmetrical partnership” in which partners consider the relationship reasonably balanced despite acknowledging enduring power imbalances. The article identifies four factors that appear to have contributed to this near-symmetrical status: the asymmetrical nature of the student exchange program; reliance on thick ties between a small number of key actors, including a boundary spanner; deliberate efforts to counter the imbalance; and positive effects of funder regulations. The conclusion presents recommendations for funders and university partners.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.242
GPT teacher head0.446
Teacher spread0.204 · 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 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 routes4
Has abstractyes

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