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Record W4402584515 · doi:10.5206/cie-eci.v53i2.17001

International Student Mobility to Canada and New Zealand: “Edugration” or “Transience”?

2024· article· en· W4402584515 on OpenAlexaffvenueabout
Conrad King, Catherine Gomes, William Shannon, Ruonan Lu

Bibliographic record

VenueComparative and International Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Policymakers in some key Western international education hubs assume that international student mobility (ISM) is based on aspirations for permanent migration, particularly if those students come from the Global South. The concept of ‘edugration’ - an amalgam of education and immigration – has become influential in both policy and research. This paper examines student motivations for ISM in Canada and New Zealand using a mixed methods approach of online surveys and focus group interviews, collecting data from 396 international student participants (Canada: n=244; New Zealand: n=152). The results show a nuanced picture, highlighting that many students view international study as a transient experience rather than one that facilitates permanent migration. The paper also discusses the extent to which a desire to attract potential migrants is reflected in policies related to ISM in the two countries, and the potential implications of the findings for these policies.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.165
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.009
Scholarly communication0.0060.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.106
GPT teacher head0.448
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 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
Published2024
Admission routes3
Has abstractyes

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