Suchergebnisse

Immigration detention is formally not a punishment, but governments do use it to deter illegal residence. This study examines whether and how immigration detention affects detainees' decision-making processes regarding departure, thereby possibly resulting in de facto "specific deterrence." Semistructured face-to-face interviews were conducted in the Netherlands with 81 immigration detainees, and their case files were examined. Evidence is found for a limited, selective deterrence effect at the level of detainee's attitudes: most respondents considered immigration detention a painful and distressing experience, but only a minority—mostly labor migrants without family ties in the Netherlands—developed a preference to return to their country of citizenship in hopes of ending their exposure, including repeated exposure, to the detention. In line with defiance theory, we find that eventual deterrent effects mostly occurred among detainees who also attributed some measure of legitimacy to their detention. Among some detainees, the detention experience resulted in a preference to migrate to a neighboring European country.

Abstract
The legitimacy of immigration systems in a globalized world is questioned since individuals without legal status are excluded from states' liberal political projects and presumed to hold beliefs different from those of the state. Drawing on ethnographic research with 105 unauthorized migrants in the Netherlands, this contribution demonstrates that these migrants may consider immigration systems to be legitimate if states lawfully enact them and provide access to their human rights. Yet, they believe that the Dutch authorities currently neglect these obligations, resulting in perceived legitimacy deficits. While some scholars advocate for new vocabulary to better understand these systems, I argue that the focus should be on changing these systems to ensure legitimacy and uphold the liberal principles of democracy.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.