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Record W4247057711 · doi:10.32920/ryerson.14647476

Betting on Canada: the immigration trajectories of Mexican professionals as international students

2021· preprint· en· W4247057711 on OpenAlexaffabout
Claudia Iveth Suarez Zamora

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsImmigrationInternational educationPolitical scienceEconomic growthInvestment (military)Marital statusSociologyHigher educationDemographyEconomicsLaw

Abstract

fetched live from OpenAlex

Canada has positioned itself as a destination for thousands of international students from all over the world. Arguably, by offering a relatively affordable education, and an inclusive society. Over the past two decades, the number of international students to Canada has not just increased, but also become more diverse by places of birth, age, marital status, education, and prior occupation. Even though many international students come to Canada when they are single young adults, some arrive with their families leaving behind professional careers back home. Using a qualitative approach, this research explores the motivations that prompt Mexican professionals to come to Canada as international students with their families. The research findings demonstrate that high levels of insecurity in Mexico was the number one push factor that motivated participants to make the decision to immigrate. Furthermore, for many this represents a significant financial investment that can require sacrifices both before and after immigrating. Key words: Mexico; international students; insecurity; immigration; professionals

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)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.004
Scholarly communication0.0070.002
Open science0.0010.007
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 head0.016
GPT teacher head0.355
Teacher spread0.339 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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