Humanitarian and Compassionate Applications: A Critical Look at Canadian Decision-Makers’ Assessment of Claims from “Vulnerable” Applicants
Bibliographic record
Abstract
For many people who have made Canada their home but have uncertain legal status and are ineligible to apply for permanent residence through other channels, the Humanitarian and Compassionate (H&C) application is the only available pathway to permanent residence and stability in Canada. Applications for permanent residence on H&C grounds have become a key component of Canada’s immigration system and yet this pathway remains under-researched. Drawing upon extensive desk research and the preliminary analysis of interview data, this article addresses this gap in the scholarship by offering a critical analysis of the H&C program. In it, we begin by discussing the specific challenges that this highly discretionary decision-making process poses for vulnerable applicants and suggest areas for improvement. We then focus on H&C applications and decisions that directly impact children and explain why a change in the Canadian application of the best interests of the child principle is required. Finally, we consider two recent trends in H&C cases: the sharp increase in the number of applications and the increasingly high rates of refusal. Throughout this analysis, we highlight the negative repercussions the current system has on the most vulnerable categories of migrants and the need to better understand these phenomena.
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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 | 0.037 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.077 | 0.030 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.013 |
| 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".