Migration and Urbanization Trends and Family Wellbeing in Canada: A Focus on Disability and Indigenous Issues
Bibliographic record
Abstract
Discussions of migration and urbanization in Canada and many other nations typically focus on the experiences of individuals. By doing so, the importance of their family relationships and circumstances may be overlooked. A failure to account for broader family networks has wellbeing consequences for both the people who migrate and/or move to urban locations and their family members who have stayed behind. Beyond the individual-level focus, policies related to migration are usually developed as population-level initiatives. This means that families that are considered vulnerable or at-risk due to certain health and/or demographic factors can remain unnoticed and their special needs unaccounted for. The experiences of these families during migration and urbanization merit greater attention so that policy makers and support services can ensure more equitable opportunities and better family wellbeing outcomes. This paper explores migration and urbanization in Canada in relation to family wellbeing with attention to two at-risk population groups: families with a member who has a disability and families that identify as Indigenous. Both groups experience exclusion, that is, systematic actions resulting in being overlooked, ignored, and at-risk. Indigenous families have endured a long history of colonialism, racism, and oppression (Saul, 2014), resulting in a legacy of grievous harm to families and the chronic underfunding of support services such as healthcare, housing, and child welfare (Government of Canada, 2018a; Truth and Reconciliation Commission of Canada, 2015). Families in which there is a member with a disability1 require ready access to affordable healthcare and related services to ensure appropriate support, which is linked to the wellbeing of all family members.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".