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Record W2789916580 · doi:10.1353/ces.2018.0007

From India to Canada: An Autoethnographic Account of an International Student’s Decision to Settle as a Self-Initiated Expatriate

2018· article· en· W2789916580 on OpenAlexvenueaboutno aff
Namita Rajani, Eddy S. Ng, Dimitria Groutsis

Bibliographic record

VenueCanadian ethnic studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsExpatriateAgency (philosophy)Settlement (finance)PopulationSociologyDeveloped countryPolitical scienceEconomic growthPublic relationsBusinessEconomicsSocial scienceLawFinance

Abstract

fetched live from OpenAlex

International students can be a source of skilled workers for many industrialized countries with an aging population. However, it is unclear if international students would stay after completing their studies, given booming economies in their home countries. The present paper explores the decision processes international students make when contemplating whether to stay or return home after completing their studies. The paper is based on an autoethnographic account of an international student who studied in Canada, and follows her journey as an international student through to settlement as a self-initiated expatriate. Initially, personal factors (such as family encouragement), public policy, and employer practices "pushed" the student to repatriate back to her country of origin after her studies. However, personal ambitions (such as travel and career development opportunities) and prospects for a better life "pulled" her back to Canada. Personal agency and positive experiences also contributed to her adjustment and success as a self-initiated expatriate. The present paper adds to existing literature on the "push" and "pull" dynamic of international student retention in the host country. It further enhances our understanding of why international students decide to remain in the country following the completion of their studies.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.126
GPT teacher head0.445
Teacher spread0.319 · 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 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

Citations13
Published2018
Admission routes2
Has abstractyes

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