The Role of Counsel in Canada’s Refugee Determination System: An Empirical Assessment
Bibliographic record
Abstract
This article examines the role of counsel in Canada’s refugee determination process through an empirical analysis of the relation between counsel and outcomes in over 70,000 refugee claims decided by the Immigration and Refugee Board from 2005-2009. The article demonstrates that competent counsel is a key factor driving successful outcomes in refugee claims. The article also shows that provincial legal aid programs are increasingly restrictive in funding legal representation for refugee claimants who cannot afford counsel. Taken together, the article argues that the increasing restrictions on legal aid put the lives of refugees at risk. The article also demonstrates that refugee claimants represented by immigration consultants are less likely to succeed than refugee claimants represented by lawyers. As a result, and in light of evidence that the immigration consulting industry has not established adequate procedures to ensure that consultants adhere to standards of professional conduct, the paper contends that immigration consultants should not be authorized to provide unsupervised representation in Canada’s refugee determination system. In short, the paper argues that counsel play an important role in the refugee determination process. Accordingly, measures should be taken to ensure that refugee claimants can access competent counsel, while simultaneously ensuring that unqualified counsel do not play a role in life and death refugee decision-making.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".