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Record W4255350919 · doi:10.32920/ryerson.14652513

The impact of Super Visa on Chinese immigrant families' settlement in Canada

2021· preprint· en· W4255350919 on OpenAlexaffabout
Ivy Li

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFamily reunificationImmigrationGrandparentCitizenshipPolitical scienceSettlement (finance)Qualitative researchImmigration policyPurchasingSociologyBusinessLawPaymentPoliticsSocial science

Abstract

fetched live from OpenAlex

In November 2011, Citizenship and Immigration Canada (CIC) announced a new Super Visa program as a successful alternative for family reunification. Scholars have criticized that Super Visa just adds more barriers to family reunification. Through qualitative interviews with immigrants who have sponsored their parents as well as parents who have come to Canada on Super Visa, this study aims to better understand the experiences of immigrant families, and make their voices heard. The key findings of this study indicate that Super Visa is helpful for family reunification especially for those whose regular visa applications are not successful. However, due to its limitations, Super Visa cannot replace the immigration sponsoring program for parents/grandparents. Some recommendations such as requesting fewer documents and information from parents and making the application easier for them, more flexible and affordable options for purchasing private medical insurance are made in order to improve the program to better serve immigrant families. Keywords: Canadian immigration policy, Super Visa program, parents/grandparents immigration, family reunification, experience of parent(s) with Super Visa, qualitative semi-structured interviews

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0250.007
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.300
Teacher spread0.291 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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