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Record W3215383902 · doi:10.1111/cag.12730

Higher education, international student mobility, and regional innovation in non‐core regions: International student start‐ups on “the rock”

2021· article· en· W3215383902 on OpenAlexafffundvenueabout
N R Graham, Yolande Pottie‐Sherman

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsMemorial University of NewfoundlandBalsillie School of International Affairs
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInternationalizationImmigrationHigher educationCorporatizationPolitical scienceEconomic growthInternational educationCore (optical fiber)Public relationsBusinessEconomicsInternational tradeEngineering

Abstract

fetched live from OpenAlex

This paper makes a case for post‐graduate international students as an increasingly important category of immigrant entrepreneur in Canada. We draw our findings from an analysis of new provincial immigrant entrepreneur programs and interviews with international student entrepreneurs in a mid‐sized city in Atlantic Canada. We argue that three forces have become increasingly relevant in shaping immigrant entrepreneurs' opportunity structures: (1) the internationalization of higher education institutions (HEIs), (2) the corporatization of HEIs, and (3) the regionalization of immigration. We show how public policy shifts in immigration and education have expanded the opportunity structure for international student start‐ups. These entrepreneurs are navigating multiple dimensions of risk that stem from being both temporary migrants and business owners.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.565

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0050.001
Open science0.0010.005
Research integrity0.0010.001
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 head0.026
GPT teacher head0.282
Teacher spread0.256 · 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

Citations10
Published2021
Admission routes4
Has abstractyes

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