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Record W4360842384 · doi:10.5206/cie-eci.v51i2.14108

Canada Learning Initiative in China (CLIC): A Consortium Approach to Increasing Students’ Participation in Education Abroad to China

2023· article· en· W4360842384 on OpenAlexaffvenueabout
Cen Huang, Wei Liu

Bibliographic record

VenueComparative and International Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChinaPreparednessStudy abroadThematic analysisScope (computer science)Political scienceMedical educationPublic relationsEconomic growthPedagogyPsychologySociologyQualitative researchMedicineSocial science

Abstract

fetched live from OpenAlex

Education abroad experiences have been found to be beneficial to students’ personal growth, intercultural awareness, and career preparedness. However, the rate of Canadian postsecondary students’ participation in education abroad has been low, particularly to Asia. Lack of financial resources and uncertainty in credit transfer are identified as common challenges that stop students from pursuing such opportunities. In this paper, the Canada Learning Initiative in China (CLIC), a Canadian consortium of 12 top Canadian research-intensive universities, is introduced as a model to increase students’ participation in study abroad in China. After an introduction to the design of the program in response to students’ expressed challenges, a thematic analysis is conducted on students’ self-reported learning experiences to glean the impact of such an initiative on students. This study shows that consortia can be utilized as an effective approach to increasing the scope and quality of students’ participation in education abroad.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.090
GPT teacher head0.454
Teacher spread0.364 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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