The Good, the Bad and the Ugly: A Preliminary Assessment of whether the Vavilov Framework Adequately Addresses Concerns of Marginalized Communities in the Immigration Law Context
Bibliographic record
Abstract
The Supreme Court of Canada's decision in Vavilov has modified the approach to judicial review in Canada. Does Vavilov address concerns by those representing marginalized communities in the administrative system? Grounding the discussion of this case in the immigration and refugee law context, this paper will look at positive opportunities (the good), missed opportunities (the bad) and potential issues to watch for (the ugly). First, a brief overview of the Vavilov framework will be provided. Then, the paper will provide some brief comments on the selection of the standard of review in Vavilov, specifically doing away with relative expertise (the good), the missed opportunity to revisit Dore (the bad), and confusion that might arise out of what constitutes a statutory appeal (the ugly). Third, I will assess the potential opportunities in the more robust reasonableness review (the good) in the immigration law context. Fourth, the paper will discuss how the court did not resolve the issue of how to address persistent discord adequately (the bad). Finally, I will provide my view on how the way the court addressed jurisdictional questions and the retention of relative expertise in the reasonableness review as potentially a site of messy confusion (the ugly).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".