Stanford Law’s Lisa Larrimore Ouellette and SHP’s Josh Salomon co-author report to address a persistent flaw in the U.S. health system: prioritizing treatment investment based on market potential rather than medical necessity.
While the attacks have likely inflicted significant damage on critical nuclear facilities in the short term, their ultimate success in halting Iran’s nuclear program is far from assured
While AI can enhance efficiency in areas like predictive maintenance and operational planning, its integration into the nuclear enterprise poses significant risks, some of which are inherent in the nature of ML.
Proceedings of the National Academy of Sciences (PNAS),
June 6, 2025
The number of acutely food insecure people worldwide has doubled since 2017, increasing demand for early warning systems (EWS) that can predict food emergencies. Advances in computational methods, and the growing availability of near-real time remote sensing data, suggest that big data approaches might help meet this need. But such models have thus far exhibited low predictive skill with respect to subpopulation-level acute malnutrition indicators. We explore whether updating training data with high frequency monitoring of the predictand can help improve machine learning models’ predictive performance with respect to child acute malnutrition by directly learning the dynamic determinants of rapidly evolving acute malnutrition crises. We combine supervised machine learning methods and remotely sensed feature sets with time series child anthropometric data from EWS’ sentinel sites to generate accurate forecasts of acute malnutrition at operationally meaningful time horizons. These advances can enhance intertemporal and geographic targeting of humanitarian response to impending food emergencies that otherwise have unacceptably high case fatality rates.
Despite his election victory, Lee faces a challenging road ahead, both personally and politically. It remains to be seen whether Lee’s administration can rise above partisan politics and rebuild public trust through meaningful reforms.
This article studies whether pure legality, stripped of normative components that are central to the rule of law, can convey perceived legitimacy to governmental institutions and activity. Through a survey experiment conducted among urban Chinese residents, it examines whether such conveyance is possible under current sociopolitical conditions in which the party-state continues to invest in pure legality without imposing legal checks on the party leadership’s political power and without corresponding investment in substantive rights or freedoms. Among survey respondents, government investment in professional and consistent law enforcement conveys meaningful amounts of political legitimacy. In fact, it does so even when it supports government activity, such as censorship of online speech, that is freedom depriving and socially controversial and even when such investment does not necessarily enhance the external predictability of state behavior. However, the legitimacy-enhancing effects of pure legality are likely weaker than those of state investment in procedural justice.
American Journal of Political Science,
May 23, 2025
The rise of social media in the digital era poses unprecedented challenges to authoritarian regimes that aim to influence public attitudes and behaviors. To address these challenges, we argue that authoritarian regimes have adopted a decentralized approach to produce and disseminate propaganda on social media. In this model, tens of thousands of government workers and insiders are mobilized to produce and disseminate propaganda, and content flows in a multidirectional, rather than a top-down manner. We empirically demonstrate the existence of this new model in China by creating a novel data set of over five million videos from over 18,000 regime-affiliated accounts on Douyin, a popular social media platform in China. This paper supplements prevailing understandings of propaganda by showing theoretically and empirically how digital technologies are transforming not only the content of propaganda, but also how propaganda materials are produced and disseminated.
Achieving minimum dietary diversity (MDD), a crucial indicator of infant and young child diet quality, remains a challenge in rural China, especially for infants aged 6–11 months. This study examined the rate of MDD attainment in rural China, identified its determinants using the Capability, Opportunity, Motivation, and Behavior (COM-B) model and Bayesian network analysis, and estimated the potential impact of improving each modifiable determinant. A multi-stage sampling design selected 1328 caregivers of infants aged 6–11 months across 77 rural townships in China. Data were collected through a cross-sectional survey via in-person household interviews. Bayesian network analysis identified key factors influencing MDD attainment and their interrelationships, while Bayesian inference estimated MDD attainment probabilities. Results showed that only 22.2 % of the sample infants attained MDD. Bayesian network analysis revealed that caregiver knowledge (a proxy of capability), self-efficacy and habits (proxies of motivation), and infant age directly influenced MDD attainment. Social support (a proxy of opportunity) indirectly promoted MDD attainment by boosting self-efficacy and habit. Notably, simultaneous improvements in knowledge, self-efficacy, and habit could increase MDD attainment by 17.6 %, underscoring the potential effectiveness of interventions focused on enhancing caregiver capability and motivation. The critically low MDD attainment rate among rural Chinese infants highlights the urgent need for targeted interventions. Strategies should prioritize enhancing caregiver feeding knowledge, self-efficacy, and habit formation to improve infant dietary diversity. Addressing these key factors could substantially boost MDD attainment in rural China.
