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Record W3209869462 · doi:10.32920/ryerson.14647080.v1

Canada's spousal sponsorship process: challenges of cross-national couples

2021· preprint· en· W3209869462 on OpenAlexaffabout
Sophie Qu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGovernment (linguistics)SpouseRacializationContext (archaeology)Political scienceImmigrationRacismFamily reunificationEconomic growthSociologyPublic relationsPoliticsLawEconomics

Abstract

fetched live from OpenAlex

Family reunification is a key objective of Canada’s Immigration and Refugee Protection Act. Despite this, cross-national couples continue to experience challenges during the spousal sponsorship process. The spousal sponsorship regime must be situated in the context of Canada’s history of racist immigration policies, and consider the nature of neo-racism, and the function of securitization. It is evident in the negative social construction of foreign spouses, and the conflation of cross-national couples with marriage fraud, that the government prioritizes fraud detection over family reunification. Interviews with ten individuals of cross-national marriages revealed challenges related to finances, emotional well-being, power imbalances, and the stigmatization of marrying a foreign spouse. The process was made more difficult by the government due to inadequate information, communication, and transparency. While processes of racialization can be seen to inform practices and policies of the spousal sponsorship system, other factors such as bureaucracy and socioeconomic status also appeared to play a role. Key words: family; spousal sponsorship; cross-national; racism; marriage fraud

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)0.007
metaresearch head score (Gemma)0.014
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 score0.320
Threshold uncertainty score0.643

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0390.008
Scholarly communication0.0070.002
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.051
GPT teacher head0.359
Teacher spread0.308 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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