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Record W2597361212 · doi:10.32674/jis.v7i1.248

Three Levels of Push-Pull Dynamics Among Chinese International Students’ Decision to Study Abroad in the Canadian Context

2017· article· en· W2597361212 on OpenAlexaffabout
Jun Mian Chen

Bibliographic record

VenueJournal of International Students · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsBrock University
FundersUniversity of Oxford
KeywordsExtant taxonStudy abroadIncentiveDynamics (music)Context (archaeology)Push and pullSociologyPublic relationsMarketingPolitical sciencePsychologyPedagogyBusinessEconomicsEngineeringGeography

Abstract

fetched live from OpenAlex

The extant literature on student migration flows generally focus on the traditional push-pull factors of migration at the individual level. Such a tendency excludes the broader levels affecting international student mobility. This paper proposes a hybrid of three levels of push-pull dynamics (micro−individual decision-making, meso−academic marketing, and macro−national marketing) to paint a more accurate picture of student migration flows. A case study of 15 semi-structured interviews with Chinese international students at a Canadian university was conducted to illuminate the underresearched reality that universities and Canada as a nation offer additional incentives, in conjunction with individual/familial reasons, for study abroad. The paper concludes with recommendations for new research directions arising from the present study.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.387

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.004
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.422
Teacher spread0.382 · 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

Citations87
Published2017
Admission routes2
Has abstractyes

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