Judgments v Reasons in Federal Court Refugee Claim Judicial Reviews: A Bad Precedent?
Bibliographic record
Abstract
This article offers an empirical examination of policies on the publication of refugee law decisions in Canada’s Federal Court. In 2015, the Court issued a notice describing the Court’s general practice of publishing written reasons in cases that the deciding judge considers as having precedential value and of issuing unpublished judgments in cases that the deciding judge does not view as precedential. In 2018, the Court reversed course and issued a new notice. This time, the Court indicated that all final decisions on the merits will be published.\nDrawing on data obtained via automated data scraping processes from thousands of online court dockets and from published decisions in cases involving refugee judicial reviews, the article examines patterns in unpublished judgments and published written reasons. Based on this analysis, the authors conclude that the Court’s earlier policy of not publishing supposedly non-precedential decisions was not successful and that this policy should not be replicated by other courts. Instead, other courts should be encouraged to follow the Federal Court’s more recent practice in this area, which entails publishing all final decisions on the merits.\nDans le présent article, nous proposons un examen empirique des politiques relatives à la publication des décisions en matière de droit des réfugiés prononcées par la Cour fédérale du Canada. En 2015, la Cour a publié un avis décrivant la pratique générale de la Cour consistant à publier des motifs écrits dans les affaires que le juge décideur considère comme ayant valeur de précédent et à produire des jugements non publiés dans les affaires que le juge décideur ne considère pas comme ayant valeur de précédent. En 2018, la Cour a fait volte-face et a publié un nouvel avis. Cette fois, la Cour a indiqué que toutes les décisions finales sur le fond seront publiées.\nEn s’appuyant sur des données obtenues par des processus de collecte de données automatisés à partir de milliers de dossiers judiciaires en ligne et de décisions publiées dans des affaires de contrôle judiciaire relatives aux réfugiés, l’article examine les tendances dans les jugements non publiés et les motifs écrits publiés. Sur la base de cette analyse, les auteurs concluent que la politique antérieure de la Cour de ne pas publier les décisions censées ne pas faire jurisprudence n’a pas été couronnée de succès et que cette politique ne devrait pas être reproduite par d’autres tribunaux. Au contraire, les autres tribunaux devraient être encouragés à suivre la pratique plus récente de la Cour fédérale dans ce domaine, qui consiste à publier toutes les décisions finales rendues sur le fond.
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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 | 0.076 | 0.455 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".