‘Flying Grannies’ and Human-Capital Citizenship: Care in Humanitarian and Compassionate Cases
Bibliographic record
Abstract
Given Canada's child care deficit, economic migration remains contingent on the unpaid care work of grandparent migrants, particularly grandmothers or ‘flying grannies’, who arrive through temporary pathways such as the super visa and often juggle multiple transnational caring obligations. However, routine pauses to the parent and grandparent sponsorship program render humanitarian and compassionate applications one of the few options available for grandparents seeking permanent residence. Yet this discretionary tool and grandparents’ multiple caregiving roles continue to be understudied. This socio-legal study, therefore, unpacks narratives of care in 171 humanitarian and compassionate grounds cases involving grandparents who applied to, considered applying, or were referred by judges and immigration officers to apply for the Super Visa. Drawing on Ellermann , we argue that the types of care that are valued and, subsequently, which ‘exceptional’ cases are granted permanent residence, reflect a human-capital citizenship logic and membership status. The subjective criteria used by judges and other ‘gatekeepers’, especially when determining the best interest of any child and hardship, reveal multiple tensions, inconsistencies and a limited notion of care that entrench stereotypes based on race, gender, culture, class and other vectors of social location. Ultimately, family reunification is deemed conditional, and grandparents are rendered temporary.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.044 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".