The ripple effects of US immigration policy on refugee children: A Canadian perspective
Bibliographic record
Abstract
Since 2016, an estimated 40,000 individuals have crossed the Canadian/U.S. border, seeking asylum, impacted by changing U.S policies on immigration.3 Some come from countries affected by the U.S. immigration ban, while others come as the result of failed refugee claims, worsening discrimination and immigration enforcement. In this perspective piece, we outline how domestic U.S policy can have rippling effects internationally, focusing on Canada. From direct health impacts from mental health and trauma, to limited access to health care, the impact on housing and employment, and finally the subtle but poignant shift in Canadian values, we argue that the impacts of U.S immigration policy are not only felt domestically, but globally. Finally, we seek to identify ways in which child health advocates and policymakers alike can support the well being of refugee claimant children across both borders.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".