Settling the Law: An Empirical Assessment of Decision-Making and Judicial Review in Canada's Refugee Resettlement System
Notice bibliographique
Résumé
In light of rising numbers in the global refugee population, as well as new ideas for reforming the international refugee regime that emphasize refugee containment, there is reason to reaffirm refugee resettlement as a solid mechanism for burden-sharing, and perhaps the only obtainable durable solution for refugees in a protracted refugee situation. Canada has operated a robust refugee resettlement program for decades and is now presenting its private sponsorship of refugees program as a model to the rest of the world. Despite the significance of Canadas resettlement program, both domestically and internationally, few studies have investigated how the program is deployed on the ground and how it is integrated within Canadas legal system. This dissertation explores, through empirical methodologies, how Canadas refugee resettlement framework operates as a legal process, with a focus on visa officer first instance decision-making and judicial review. The dissertation also investigates the role of refugee resettlement within Canadas broader refugee policy and explores the evolving dynamics within the private sponsorship of refugees program. The analysis relies primarily on a dataset of 403 Federal Court judicial review court files submitted by rejected resettlement applicants between 2011 and 2015. The data on visa officer decision-making reveals concerning trends in various areas of decision-making, including documenting decisions, assessing credibility, assessing objective evidence, and dealing with language barriers and gender-based claims. The data also shows problematic interpretations of legislative criteria, including local integration, successful establishment, and inadmissibility. These shortcomings have serious consequences for refugee applicants, who are entitled to a fair and accurate decision, but also for sponsors in Canada, whose commitment for sponsorship may fade in the face of repeated problematic decisions. The analyses of judicial review outcomes show that leave grant rates are much higher in overseas refugee cases than in inland cases. The data also shows that some extra-legal factors, including lawyer experience and city of filing, are correlated to variations in outcomes. The wide variation in grant rates among individual Federal Court judges observed in the inland refugee context is also partly reflected in the dataset. In a more general sense, the data suggests that judicial review plays a limited role in the refugee resettlement program because of various legal and practical factors. Very few resettlement applicants have the financial or informational resources to pursue judicial review. As such, judicial review is an inaccessible avenue for the vast majority of resettlement applicants. Access to judicial review is further limited by the leave requirement, which deprives more than a third of applicants from having their case heard on the merits. In addition, the government settles out of court a surprisingly high proportion of cases. This practice raises concerns around IRCCs potential use of case settlement as a method to insulate objectionable practices from judicial and public scrutiny, and avoid restrictive precedents. Considering the important practical difficulties refugee applicants face in accessing judicial review, this dissertation suggests that the most promising avenue for improving the legal infrastructure of refugee resettlement is to strengthen first instance decision-making through administrative changes. A few recommendations are offered in this regard, including increasing visa officer training, reverting to the automatic internal review of refusals, and instituting audio recording of interviews. I also argue that two regulatory changes are needed in order to bring Canadas resettlement framework more in line with UNHCR guidelines and the principle of refugee protection, namely the elimination the successful establishment and the UNHCR documentation requirements.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,062 | 0,271 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,008 | 0,014 |
| Études des sciences et des technologies | 0,021 | 0,016 |
| Communication savante | 0,021 | 0,004 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».