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Humanitarian Violence
When is a war not a war? When it is undertaken in the name of democracy, against the forces of racism, sexism, and religious and political persecution? This is the new world of warfare that Neda Atanasoski observes inHumanitarian Violence, different in name from the old imperialism but not so different in kind. In particular, she considers U.S. militarism-humanitarianmilitarism-during the Vietnam War, the Soviet-Afghan War, and the 1990s wars of secession in the former Yugoslavia.
What this book brings to light-through novels, travel narratives, photojournalism, films, news media, and political rhetoric-is in fact a system of postsocialist imperialism based on humanitarian ethics. In the fiction of the United States as a multicultural haven, which morally underwrites the nation's equally brutal waging of war and making of peace, parts of the world are subject to the violence of U.S. power because they are portrayed to be homogeneous and racially, religiously, and sexually intolerant-and thus permanently in need of reform. The entangled notions of humanity and atrocity that follow from such mediations of war and crisis have refigured conceptions of racial and religious freedom in the post-Cold War era. The resulting cultural narratives, Atanasoski suggests, tend to racialize ideological differences-whereas previous forms of imperialism racialized bodies. In place of the European racial imperialism, U.S. settler colonialism, and pre-civil rights racial constructions that associated racial difference with a devaluing of nonwhite bodies,Humanitarian Violenceidentifies an emerging discourse of race that focuses on ideological and cultural differences and makes postsocialist and Islamic nations the potential targets of U.S. disciplining violence.
eBook
Giving circles : philanthropy, voluntary association, and democracy
In the contemporary United States, third parties are being relied upon to
deliver social services that were once chiefly the responsibility of government.
Among the new philanthropic associations that have arisen in this environment are
voluntary groups known as giving circles. Their purpose is to bring people together
to pool resources and then collectively decide how to distribute them. Giving
circles have been seen as the most democratic of philanthropic mechanisms, working
to meet social needs and solve community problems, while enhancing the civic
education and participation of their members. Angela M. Eikenberry examines this new
phenomenon and considers what role voluntary associations and philanthropy can or
should play in a democratic society.
eBook
Evaluating the impact of two decades of USAID interventions and projecting the effects of defunding on mortality up to 2030: a retrospective impact evaluation and forecasting analysis
The US Agency for International Development (USAID) is the largest funding agency for humanitarian and development aid worldwide. The aim of this study is to comprehensively evaluate the effect of all USAID funding on adult and child mortality over the past two decades and forecast the future effect of its defunding.
In this retrospective impact evaluation integrated with forecasting analysis, we used panel data from 133 countries and territories— including all low-income and middle-income countries (LMICs)—with USAID support ranging from none to very high. First, we used fixed-effects multivariable Poisson models with robust SEs adjusted for demographic, socioeconomic, and health-care factors to estimate the impact of USAID funding on all-age and all-cause mortality from 2001 to 2021. Second, we evaluated its effects by age-specific, sex-specific, and cause-specific groups. Third, we did several sensitivity and triangulation analyses. Lastly, we integrated the retrospective evaluation with validated dynamic microsimulation models to estimate effects up to 2030.
Higher levels of USAID funding—primarily directed toward LMICs, particularly African countries—were associated with a 15% reduction in age-standardised all-cause mortality (risk ratio [RR] 0·85, 95% CI 0·78–0·93) and a 32% reduction in under-five mortality (RR 0·68, 0·57–0·80). This finding indicates that 91 839 663 (95% CI 85 690 135–98 291 626) all-age deaths, including 30 391 980 (26 023 132–35 482 636) in children younger than 5 years, were prevented by USAID funding over the 21-year study period. USAID funding was associated with a 65% reduction (RR 0·35, 0·29-0·42) in mortality from HIV/AIDS (representing 25·5 million deaths), 51% (RR 0·49, 0·39–0·61) from malaria (8·0 million deaths), and 50% (RR 0·50, 0·40–0·62) from neglected tropical diseases (8·9 million deaths). Significant decreases were also observed in mortality from tuberculosis, nutritional deficiencies, diarrhoeal diseases, lower respiratory infections, and maternal and perinatal conditions. Forecasting models predicted that the current steep funding cuts could result in more than 14 051 750 (uncertainty interval 8 475 990–19 662 191) additional all-age deaths, including 4 537 157 (3 124 796–5 910 791) in children younger than age 5 years, by 2030.
USAID funding has significantly contributed to the reduction in adult and child mortality across low-income and middle-income countries over the past two decades. Our estimates show that, unless the abrupt funding cuts announced and implemented in the first half of 2025 are reversed, a staggering number of avoidable deaths could occur by 2030.
The Spanish Ministry of Science and Innovation, UK Medical Research Council, and EU Horizon Europe.
Journal Article
Multivariate random forest prediction of poverty and malnutrition prevalence
Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming. However, state of the art models often rely on proprietary data and/or deep or transfer learning methods whose underlying mechanics may be challenging to interpret. We demonstrate how interpretable random forest models can produce estimates of a set of (potentially correlated) malnutrition and poverty prevalence measures using free, open access, regularly updated, georeferenced data. We demonstrate two use cases: contemporaneous prediction, which might be used for poverty mapping, geographic targeting, or monitoring and evaluation tasks, and a sequential nowcasting task that can inform early warning systems. Applied to data from 11 low and lower-middle income countries, we find predictive accuracy broadly comparable for both tasks to prior studies that use proprietary data and/or deep or transfer learning methods.
Journal Article