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Record W4410892691 · doi:10.1080/1369183x.2025.2503960

Contextualising international student migration to Canada: the case of Indian Punjab youths

2025· article· en· W4410892691 on OpenAlexafffundabout
Kriti Sharma, Ito Peng

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsIrregular migrationPolitical scienceDemographic economicsGeographyGender studiesCriminologySociologySocioeconomicsEconomic geographyEconomics

Abstract

fetched live from OpenAlex

The magnitude and persistence of the international student flow from the Indian Punjab to Canada over the last decade presents a compelling case for International Student Migration research, yet empirical data on this specific phenomenon is scant. We address this gap through a thematic analysis based on interviews with 34 Punjabi students aspiring for Canadian education. We investigate Punjabi student migration as the complex interplay between individual motivation and the social and territorial structures in which students are located when they decide to migrate. We apply the aspirations-capabilities framework to highlight the ways in which bounded individual agency is exercised by students within national and international policy contexts. Our analysis reveals a blurring of the line between ‘migration for education’ and ‘education for (im)migration’. While many students sought economic benefits, they also aimed to escape Punjab for personal growth in a more ‘liberal’ and ‘modern’ society, a desire especially strong among women. At the micro level, students’ perceived capabilities (e.g. part-time work, reliance on social networks), influence their real financial capabilities; at the macro level, Canada's previously permissive immigration policies made it a preferred destination. However, recent restrictive changes in immigration policy create new challenges for Punjabi students’ education-migration aspirations.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.047
GPT teacher head0.408
Teacher spread0.362 · 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

Citations3
Published2025
Admission routes3
Has abstractyes

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