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‘A security built in the dark’: operazione strade sicure and the productivity of ignorance

ABSTRACT Over the last thirty years, borders have become increasingly diversified: border controls no longer take place only at state borders, but also within state territories. This paper critically examines the Italian military operation ‘Strade Sicure’ as an example of urban bordering processes. Launched in 2008 to address a ‘perception of insecurity’ among the Italian population, the operation has included controls of migrant reception centers and patrols in ethnically diverse neighborhoods. Based on an analysis of 442 articles published by the two largest Italian dailies, I apply Fassin’s moral economy of migration as a lens to analyze the imaginaries, affects, and knowledges that underpin this military operation. I argue that the debates surrounding the operation reveal an imaginary of a white Italian nation at war with racialized ‘Others’ who threaten its security, identity, culture, and health. At the heart of this moral economy of migration is a widely unquestioned economy of fear. By problematizing this fear as entangled with white ignorance, this paper seeks to highlight the importance of critically engaging with how particular kinds of (non-)knowledge inform imaginaries of migration.

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Matching-Assisted Power Prior for Incorporating Real-World Data in Randomized Clinical Trial Analysis.

Leveraging external data information to supplement randomized clinical trials has been a popular topic in recent years, especially for medical device and drug discovery. In rare diseases, it is very challenging to recruit patients and run a large-scale randomized trial. To take advantage of real-world data from historical trials on the same disease, we can run a small hybrid trial and borrow historical controls to increase the power. But the borrowing needs to be conducted in a statistically principled manner. Bayesian power prior methods and propensity score adjustments have been discussed in the literature. In this paper, we propose a matching-assisted power prior approach to better mitigate observed bias when incorporating external data. A subset of comparable external subjects is selected by groups through template matching, and different weights are assigned to these groups based on their similarity to the current study population. Power priors are then implemented to incorporate the information into Bayesian inference. Unlike conventional power prior methods, which discount all control patients similarly, matching pre-selects good controls, hence improved the quality of external data being borrowed. We compare its performance with the existing propensity score-integrated power prior approach through simulation studies and illustrate the implementation using data from a real acupuncture clinical trial.

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