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As immigration polices increasingly entangle with those of higher education (HE), institutions are being positioned as migrant surveillance actors. HE's participation in state-managed international student compliance regimes (ISCRs), for example, raises political and ethical questions, including those concerning the core mission of HE. This paper traces the Canadian ISCR context over the past decade, focusing on the introduction of the Designated Learning Institution (DLI) student compliance reporting requirement in 2014. It argues that Canadian HE's role in surveilling temporary residents on behalf of the state is a problematic bordering practice. It also posits that this new form of surveillance is not necessarily a novel compromise of HE's values, but rather connected to historical patterns of HE's involvement in border imperialism. It questions the benevolence of both Canada's immigration policies towards international students and, ultimately, higher education itself.

This paper investigates how to best compare algorithms for predicting chronic homelessness for the purpose of identifying good candidates for housing programs. Predictive methods can rapidly refer potentially chronic shelter users to housing but also sometimes incorrectly identify individuals who will not become chronic (false positives). We use shelter access histories to demonstrate that these false positives are often still good candidates for housing. Using this approach, we compare a simple threshold method for predicting chronic homelessness to the more complex logistic regression and neural network algorithms. While traditional binary classification performance metrics show that the machine learning algorithms perform better than the threshold technique, an examination of the shelter access histories of the cohorts identified by the three algorithms show that they select groups with very similar characteristics. This has important implications for resource constrained not-for-profit organizations since the threshold technique can be implemented using much simpler information technology infrastructure than the machine learning algorithms. [ABSTRACT FROM AUTHOR]