Not Just the Luck of the Draw? Exploring Competency of Counsel and Other Qualitative Factors in Federal Court Refugee Leave Determinations (2005-2010)
Bibliographic record
Abstract
Refugee claimants who have received a negative decision from the Immigration and Refugee Board sometimes seek judicial treview at the Federal Court in Canada. Previous statistical studies, in particular Sean Rehaag’s (2012) study, “The Luck of the Draw,” have reported that rejected refugee claimants seeking judicial review face low and inconsistent leave grant rates, with chances of success largely dependent on judge assignment. The present research looks beyond these quantitative findings to identify additional factors that may explain the troubling statistics. To this end, four researchers manually reviewed 50 leave applications submitted between 2005 and 2010 and included in Rehaag’s (2012) data set. The results of this qualitative analysis are disturbing: a significant number of rejected leave applications had been poorly prepared, and a number of facially strong cases were denied leave. These results suggest that leave grant rates could rise if the quality of legal representation were enhanced. They also indicate that rejected refugee claimants would benefit from clear and uniformly applied criteria for granting leave.
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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 | 0.025 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 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".