Opportunistic oppression: U.S. migration restrictions and public health policy during the COVID-19 pandemic

As the COVID-19 pandemic swept the world in Spring 2020, the Trump administration invoked war against the coronavirus to severely restrict admission of migrants and asylum seekers into the United States. At the same time, it declined to enact national measures to control viral community spread and sharply criticised public health policies. We analyse this notable inconsistency as a case of opportunistic oppression whereby policymakers take advantage of a crisis to pursue pre-existing, and often unrelated, policy preferences. We identify how the securitisation of health and the crisis-enabled politics of enmity allowed the Trump administration to cynically erode migrant human rights protections while simultaneously failing to contain the pandemic. Opportunistic oppression represents an attractive strategy for states facing real and imagined emergencies to pursue political agendas that are not necessarily part of a coherent and effective response to the crisis at hand. [ FROM AUTHOR] Copyright of International Journal of Human Rights is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Towards a predictive model of COVID-19 vaccine hesitancy among American adults.

Designing effective public health campaigns to combat COVID-19 vaccine hesitancy requires an understanding of i) who the vaccine hesitant population is, and ii) the determinants of said population's hesitancy. While researchers have identified a number of variables associated with COVID-19 vaccine hesitancy that could inform such campaigns, little is known about the cumulative or relative predictive power of these factors. In this article, we employ a machine learning model to analyze online survey data collected from 3353 respondents. The model incorporates an array of variables that have been shown to impact vaccine hesitancy, allowing us to i) test how well we can predict vaccine hesitancy, and ii) compare the relative predictive impact of each covariate. The model allows us to correctly classify individuals that are vaccine acceptant with 97% accuracy, and those that are vaccine hesitant with 72% accuracy. Trust in and knowledge about vaccines is, by far, the strongest predictor of vaccination choice. While our results demonstrate that public health campaigns designed to increase vaccination rates must find a way to increase public trust in COVID-19 vaccines, our results cannot speak to the malleability of such beliefs, nor how to enhance trust.

The belief that politics drive scientific research & its impact on COVID-19 risk assessment.

We use survey data collected from 12,037 US respondents to examine the extent to which the American public believes that political motives drive the manner in which scientific research is conducted and assess the impact that such beliefs have on COVID-19 risk assessments. We find that this is a commonly held belief and that it is negatively associated with risk assessments. Public distrust in scientists could complicate efforts to combat COVID-19, given that risk assessments are strongly associated with one's propensity to adopt preventative health measures.