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The Agreement between the Government of Canada and the Government of the United States of America for cooperation in the examination of refugee status claims from nationals of third countries (Safe Third Country Agreement) has been subject to criticism since its adoption. Aimed at addressing asylum shopping, the agreement prevents asylum seekers, who have transited through one country, from applying for asylum in the other, subject to certain exceptions. The fact that the agreement applies only to land border ports of entry has led to an increase in irregular entries into Canada, particularly since 2017. The agreement has been the subject of numerous legal challenges. Against this backdrop, the authors analyze the implementation of the agreement in Canadian law and the issues it raises. They argue that the agreement is counterproductive, as if fails to effectively promote the orderly processing of refugee claims from the United States. They also examine the historical background of the numerous legal challenges to the agreement, including the latest Federal Court decision of 2020 which found that the agreement violates section 7 of the Canadian Charter of Rights and Freedoms.
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Disclaimer: This summary was generated by AI based on the content of the source document.
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Disclaimer: This summary was generated by AI based on the content of the source document.
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Disclaimer: This summary was generated by AI based on the content of the source document.
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Judges are obliged to follow the law, including by imposing mandatory minimum sentences. This can create a moral dilemma. Judges who justify punishment based on its utility in reducing crime may believe that minimum sentences are pointlessly harsh because they do not enhance public protection. Judges who justify punishment based on retributivist theory may feel that mandatory minimum sentences are excessive and therefore unjust. This paper contends that in their effort to impose just and useful sentences, judges tend to seek out and use available legal tools to reduce perceived unfairness. Statutory interpretation, the use of the principle of totality in multiple count prosecutions, and the selective deployment of sentencing tools have all been used to this end. The author contends that striving to constrain the impact of minimum sentences in this way does not corrupt the administration of justice. Instead it is a legitimate, predictable and inevitable outcome.
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Rendered in December 2019, the Vavilov decision sets the contemporary analytical framework for the judicial review of an administrative decision on the merits. On this occasion, the Supreme Court expressed the desire to add a certain degree of certainty and consistency to this field of law. This article focuses on the new approach’s propensity to achieve that goal. The analysis begins with the observation that there exists a connection between the instability that has historically characterized the law of judicial review and the failure of previous approaches to adequately guide reviewing courts in the fulfillment of their mission, which consists of balancing the rule of law and legislative supremacy. In light of earlier case law, the author concludes that the Vavilov decision offers the prospect of greater certainty in determining the applicable standard of review, but that the risk of instability remains with respect to the application of the reasonableness standard. Indeed, while the Supreme Court’s guidance in this regard generally reflects a concern to alleviate the tension underlying the relationship between rule of law and legislative supremacy principles, some of the majority justices’ assertions, namely that certain statutory provisions “relating to the scope of a decision maker’s authority” may involve only one interpretation, may weaken the self-discipline of reviewing courts on which judicial deference is based.
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Disclaimer: This summary was generated by AI based on the content of the source document.
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Disclaimer: This summary was generated by AI based on the content of the source document.
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Disclaimer: This summary was generated by AI based on the content of the source document.
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