Canadian Visa Officers and the Social Construction of “Real” Spousal Relationships
Bibliographic record
Abstract
Cet article examine l'existence d'un paradoxe de facilitation/contrôle dans le cadre des efforts gouvernementaux de contrôler la migration des époux. La littérature sur le jugement discrétionnaire et les fonctionnaires de proximité est utilisée pour analyser comment les agents canadiens des visas décident quelles relations de couples sont “vraies” ou “fausses”. Cet article démontre que les agents des visas utilisent plusieurs cadres de référence pour construire des modèles types de ce que sont les relations “normales” entre époux. Ces modèles types permettent aux agents des visas de construire des cas comme crédibles ou non crédibles, et ce processus établit les bases des décisions concernant l'exclusion et l'inclusion d'immigrants. This paper questions whether there is a facilitation/enforcement paradox associated with state efforts to control spousal migration. The literature on discretion and street‐level bureaucrats is used to analyze how Canadian visa officers make decisions about which spousal relationships are “real” and which are “fake.” The paper shows that visa officers use various cultural frameworks to construct typifications of “normal” relationships. These typifications allow visa officers to construct cases as credible or not credible, and constitute simultaneous bases for decisions about immigrant exclusion and inclusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".