We investigate the possibility of defining meaningful upper and lower quantization dimensions for a compactly supported Borel probability measure of order r, including negative values of r. To this end, we employ the concept of partition functions, which generalises the notion of the $L^q$-spectrum, thus extending the authors’ earlier work with Sanguo Zhu in a natural way. In particular, we derive inherent fractal-geometric bounds and easily verifiable necessary conditions for the existence of quantization dimensions. We state the exact asymptotics of the quantization error of negative order for absolutely continuous measures, thereby providing an affirmative answer to an open question regarding the geometric mean error posed by Graf and Luschgy in this journal in 2004.

The Collaborating Network of Networks for Evaluating COVID-19 and Therapeutic Strategies (CONNECTS) was a novel network of networks created in response to the COVID-19 pandemic. This program brought together a large matrix of clinical research networks to swiftly design and/or implement concurrent clinical studies. Successful coordination of this large-scale collaboration required innovative solutions for timely and transparent centralized operations reporting. As the Administrative Coordinating Center (ACC) for CONNECTS, RTI International developed and maintained a web-based infrastructure that served as the central communication and reporting hub. This single-platform approach provided a robust collection of key topics to support daily operational oversight (e.g., enrollment and retention, site coverage, study milestones, financial tracking). Underlying data acquisition, harmonization, and portal reporting methods aimed to address nuances of the network of networks (e.g., disparate data sources, diverse user needs) while providing the necessary speed and agility to combat the COVID-19 pandemic. The resulting system was well received, readily adapted to changes, and successfully supported three years of early COVID-19 research. Although the CONNECTS central reporting methods arose from necessity during an urgent and dynamic public health emergency, they are a model for efficient and effective centralized operational reporting for any large-scale public health research effort.

The recent proliferation of the digital video essay as a public, personalized, and creative genre on YouTube testifies to the immense popularity of essayistic practices across the globe today. Some of today’s digital video essays from this platform engage in what I call “postscholarly” criticism, which combines the nonprofessional explorations of literary humanities scholarship with personal, social, and public commentary. My essay broadly argues that, by situating academic scholarship in the global digital public domain, today’s author-critics produce self-appointed sites of popular humanities work. To examine the tension between disciplinarity and publicity of postscholarly video essays, I theorize the influences of the academic humanities on today’s author-critics, along with exploring the amateur and public origins of the video essay production. By drawing on relevant examples, I aim to show the salient traits of postscholarly criticism from YouTube, with a focus on its global transmission. Finally, I raise the question of (post-)professionalism within literary public humanities to promote its distinction from today’s popular public practices of criticism. Overall, my essay invites scholars of the public humanities to study the new digital era of postscholarly criticism as a popular phenomenon with significant implications for the literary discipline and beyond.

Geographic information systems (GIS) are computer-based spatial mapping tools widely used in public health to examine service availability and access disparities and healthcare utilization. While GIS has supported evidence-based health planning in various domains, its application in mental healthcare service delivery remains underexplored. Our scoping review aimed to address this gap by exploring the scope and type of GIS usage in studying three dimensions of mental health (MH) service delivery (availability, accessibility and utilization), across all geographical locations, settings and populations. We conducted a scoping review following the Joanna Briggs Institute methodology. We included peer-reviewed English-language studies using GIS to examine service delivery (availability, accessibility or utilization) for any MH condition diagnosed through standardized criteria or validated tools. Seven databases were searched (Medical Literature Analysis and Retrieval System Online [MEDLINE], PsycINFO, Excerpta Medica Database [Embase], Global Health, Cumulative Index to Nursing and Allied Health Literature [CINAHL], Cochrane Central Register of Controlled Trials [CENTRAL] and Web of Science) between January and April 2024. This review included 58 studies predominantly from high-income countries. A wide range of GIS methods were employed across studies, including hotspot analysis, network analysis and spatial analysis. Six studies explored availability, generally through measures like distribution of facilities across a population, and resource availability within 5–10-mile network buffers. Forty-six studies explored the spatial accessibility of MH services and substance-use treatment facilities using GIS. Six studies examined service utilization patterns. Equity emerged as a recurring theme across all three dimensions. GIS has the potential to emerge as a powerful tool in MH research, particularly in mapping disparities, informing service delivery and identifying high-risk zones. Expanding GIS use in trial design, implementation science and policy advocacy could help bridge critical gaps in MH service delivery, ensuring more equitable and data-driven decision-making.

Our research reviews theory and evidence in the economics literature to provide a standard value of a statistical life (VSL) applicable to the Department of Defense (DOD). We follow Viscusi (Best estimate selection bias in the value of a statistical life, Journal of Benefit-Cost Analysis, 9(2), 205–246, 2018a) by conducting a meta-analysis of 1,025 VSL estimates from 68 different labor market studies and find a best-set average VSL estimate of $11.8 million (US$2021) across all studies. For DOD analysts and practitioners, we advocate using our best-set VSL estimate for the vast majority of benefit–cost analyses (BCAs) within the DOD. In addition to providing a VSL benchmark to use in DOD BCAs, we disaggregate casualty types and provide a range of VSL estimates to use in sensitivity analyses. Employing restricted data from the DOD on over 6,700 US military fatalities in Afghanistan and Iraq from 2001 to 2021, we show that (1) fatalities are highly concentrated among young, White and enlisted males, and that (2) the Army and Marines account for the vast majority of the fatality totals (73 and 22%, respectively), in contrast to the low number of fatalities (<5%) in the Air Force and Navy. The monetized cost of US military fatalities in Afghanistan and Iraq would involve individual VSL levels that range from $3.2 to $27.6 million per statistical life (US$2021), after applying standard pay grade and income adjustments.

This study examines the representation of psychotic disorders in Spanish punk music over three decades, analysing 5647 songs from 177 bands. Content related to psychotic disorders appeared in 2.28% of the corpus, divided into songs with psychosis as a central theme and those using psychopathological terms incidentally. Schizophrenia and paranoia were the most referenced diagnoses, although frequently applied in ways that lacked clinical accuracy. Thematic analysis revealed two main dimensions: a clinical–therapeutic one, typically negative in tone, centred on symptoms, suffering, treatments, hospital admission and substance use; and a social dimension, highlighting stigma, rejection, loneliness and incomprehension. Although many songs linked psychosis to violence and crime, others framed it as a source of wisdom, freedom or creativity. Overall, punk music offers a complex and polarised discourse on mental illness, reflecting societal perceptions that oscillate between empathy and the reinforcement of stereotypes.

Advances in deep learning and representation learning have transformed item factor analysis (IFA) in the item response theory (IRT) literature by enabling more efficient and accurate parameter estimation. Variational autoencoders (VAEs) are widely used to model high-dimensional latent variables in this context, but the limited expressiveness of their inference networks can still hinder performance. We introduce adversarial variational Bayes (AVB) and an importance-weighted extension (IWAVB) as more flexible inference algorithms for IFA. By combining VAEs with generative adversarial networks (GANs), AVB uses an auxiliary discriminator network to frame estimation as a two-player game and removes the restrictive standard normal assumption on the latent variables. Theoretically, AVB and IWAVB can achieve likelihoods that match or exceed those of VAEs and importance-weighted autoencoders (IWAEs). In exploratory analyses of empirical data, IWAVB attained higher likelihoods than IWAE, indicating greater expressiveness. In confirmatory simulations, IWAVB achieved comparable mean-square error in parameter recovery while consistently yielding higher likelihoods, and it clearly outperformed IWAE when the latent distribution was multimodal. These findings suggest that IWAVB can scale IFA to complex, large-scale, and potentially multimodal settings, supporting closer integration of psychometrics with modern multimodal data analysis.

The question of how to pursue politically relevant and engaged scholarship has been an ongoing theme within socio-legal scholarship. In the United States of America, Presidential Addresses of the Law and Society Association have consistently urged greater political engagement (Lempert 2001; Seron 2016; Scheppele 2023). In the United Kingdom, journals such as Social & Legal Studies have placed critical and engaged scholarship at the core of their mission (Editorial 1992; Editorial 1998). In the Majority World1 – where the socio-legal field is less institutionalised – scholars have often been more directly involved in political action, re-imagining colonial law and using it as a tool for social change (Shivji 2018; Sieder, Ansolabehere and Alfonso Sierra 2019). These scholars have not only inspired calls for activist scholarship in the Minority World (Munger 2001) but have also unsettled the very dichotomy between scholarship and activism (D’Souza 2009).

This study presents the creation and validation of LexEst, a short 5-minute test to assess vocabulary knowledge in Estonian. Our freely accessible test consists of 90 items and is designed for L2 speakers of Estonian. LexEst is modeled after the original Lexical Test for Advanced Learners of English. Similarly to other test variants, our test has been adapted to assess vocabulary knowledge at varying proficiency levels. Our findings demonstrate that LexEst provides an objective measure of the Estonian vocabulary of L2 learners, aligning well with subjective language proficiency indicators, such as self-assessed skills. In addition, higher LexEst scores and shorter response times are associated with higher CEFR-level language courses and a greater daily use of Estonian. Higher LexEst scores are also associated with an earlier age of acquisition in Estonian and a higher perceived importance of learning Estonian.

Political polarization is a group phenomenon in which opposing factions, often of unequal size, exhibit asymmetrical influence and behavioral patterns. Within these groups, elites and masses operate under different motivations and levels of influence, challenging simplistic views of polarization. Yet, existing methods for measuring polarization in social networks typically reduce it to a single value, assuming homogeneity in polarization across the entire system. While such approaches confirm the rise of political polarization in many social contexts, they overlook structural complexities that could explain its underlying mechanisms. We propose a method that decomposes existing polarization and alignment measures into distinct components. These components separately capture polarization processes involving elites and masses from opposing groups. Applying this method to Twitter discussions surrounding the 2019 and 2023 Finnish parliamentary elections, we find that (1) opposing groups rarely have a balanced contribution to observed polarization, and (2) while elites strongly contribute to structural polarization and consistently display greater alignment across various topics, the masses, too, have recently experienced a surge in alignment. Our method provides an improved analytical lens through which to view polarization, explicitly recognizing the complexity of and need to account for elite-mass dynamics in polarized environments.