The American K12 education system has long been seen as centralized and rigid. The 1990s saw the emergence of several changes, which arguably pushed it in a potentially more polycentric, localized direction. Many school choice policies have developed since then, and a large amount of research has been conducted on these trends in American education since 1991. Just prior to these changes, in 1991, Davis and Ostrom published ‘A Public Economy Approach to Education: Choice and Co-Production’. That work sought to examine the extent of co-production in American schools to that time, and the extent to which the system was polycentric. This paper seeks to use Davis & Ostrom’s framing and to update their work in the current context. U.S. education policy has generally become more decentralized during this time, but not consistently. This paper also finds that U.S. education policy and practice has in fact developed along several of the lines Davis & Ostrom predicted.

Testitems using open-ended response formats can increase an instrument’s construct validity. However, traditionally, their application in educational testing requires human coders to score the responses. Manual scoring not only increases operational costs but also prohibits the use of evidence from open-ended items to inform routing decisions in adaptive designs. Using machine learning and natural language processing, automatic scoring provides classifiers that can instantly assign scores to text responses. Although optimized for agreement with manual scores, automatic scoring is not perfectly accurate and introduces an additional source of error into the response process, leading to a misspecification of the measurement model used with the manual score. We propose two joint models for manual and automatic scores of automatically scored open-ended items. Our models extend a given model from Item Response Theory for the manual scores by a component for the automatic scores, accounting for classification errors. The models were evaluated using data from the Programme for International Student Assessment (2012) and simulated data, demonstrating their capacity to mitigate the impact of classification errors on ability estimation compared to a baseline that disregards classification errors.

Precision medicine (PM) encompasses various emerging, data-intensive healthcare and biomedical research initiatives aimed at tailoring care to individual patient characteristics. While “precision” primarily denotes an epistemological shift in how biomedical research is approached and care delivered, the conviction that PM empowers patients in clinical decision-making is central to its vision as it is taken up across policy contexts. In this paper, I critically assess these promises by drawing on recent engagements in agential epistemic injustice (Lackey 2020; Medina 2022). I suggest that the social, cultural, and epistemological conditions in which the epistemic practice of precision care unfolds are conducive to epistemic injustice. Despite PM’s explicit aims to address longstanding criticisms regarding the disease-centric nature of contemporary biomedical care practices by including person-centered, non-biomedical features in clinical consideration, exploring its underlying logic suggests its epistemic economy is stacked against patients’ epistemic interests. As such, despite its laudable aim of patient empowerment, the exacerbated risk of epistemic injustice might truncate patients’ (epistemic) agency, further disempowering them in clinical decision-making. To conclude, I suggest that the reliance on “empowerment”- and “person-centered care”-rhetoric dominating PM discourse is a case of epistemic appropriation (Davis 2018), further discouraging engagement with social, experiential, and phenomenological dimensions of illness, defanging critics of raising those concerns, and impeding the realization of epistemic justice in healthcare.

Do social media offer more opportunities for parliamentary opposition and independent candidates to reach voters in electoral autocracies? Social media have been seen as a great liberation tool, facilitating the mobilisation of disenfranchised citizens. However, scholarship on electoral autocracies highlights how they are well-versed in subverting democratic innovations. Taking the 2021 legislative campaign in Uganda as a case, we show that social media offer a range of opportunities for the opposition to campaign, while also providing new ways for the regime to try to maintain its dominance. Our findings rely on insights from 35 interviews with legislative candidates combined with data collected from their Facebook pages and Twitter profiles as well as from those of their opponents. We contribute to the literature on electoral autocracy and on candidates' use of social media in electoral campaigns by identifying the opportunities social media offer for both the regime and its opposition.

Climbing aroids, despite their abundance in tropical forests, remain underexplored. This study is focused on species richness, abundance, density, and distribution patterns of climbing aroid community in a lowland rainforest in Los Tuxtlas, Veracruz, Mexico. Over two years, two censuses were conducted across 14 plots, recording 12 aroid species from five genera and their potential hosts. Ontogenetic classes were defined and validated, showing a positive correlation between total plant length/apex height and ontogenetic stage, indicating distinct growth phases. Host size (DBH) was significant predictor of the establishment probability across ontogenetic classes. Vertical distribution varied significantly among species, ranging from Philodendron hederaceum (7 m) to Anthurium flexile (0.88 m), with some species predominantly distributed on specific host sizes. Tree falls impacted specific species. Although limitations, including a short study period, restrict broader generalizations, this research establishes a foundational understanding of climbing aroid ecology and underscores the need for standardized methods and long-term monitoring to elucidate their population dynamics and ecological strategies.

Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on the dynamics of multiple outcomes measured frequently over time. Motivated by an mHealth study in which participants self-report the intensity of many emotions multiple times per day, we describe a dynamic factor model that summarizes ILD as a low-dimensional, interpretable latent process. This model consists of (i) a measurement submodel—a factor model—that summarizes the multivariate longitudinal outcome as lower-dimensional latent variables and (ii) a structural submodel—an Ornstein–Uhlenbeck (OU) stochastic process—that captures the dynamics of the multivariate latent process in continuous time. We derive a closed-form likelihood for the marginal distribution of the outcome and the computationally-simpler sparse precision matrix for the OU process. We propose a block coordinate descent algorithm for estimation and use simulation studies to show that it has good statistical properties with ILD. Then, we use our method to analyze data from the mHealth study. We summarize the dynamics of 18 emotions using models with one, two, and three time-varying latent factors, which correspond to different behavioral science theories of emotions. We demonstrate how results can be interpreted to help improve behavioral science theories of momentary emotions, latent psychological states, and their dynamics.

Latin American countries have pioneered innovations in social protection, but their welfare institutions suffer from large and persistent gaps and inequalities in access and provision. This article reviews the substantive body of research addressing this anomaly. A focus on social protection offers a window on what is distinctive about social policy in the region. The social protection matrix in Latin America combines three core institutions: occupational insurance funds, personal pensions and social assistance. The article highlights the role of political realignments shaping current institutions. The critical review yields several pointers for a ‘general’ theory of welfare institutions.

The purpose of the South African Competition Act is to resolve the present problems of inequality by emphasizing its multiple goals, which differ from those of other countries. Its objectives broadly contain efficiency, state economic development and consumer welfare. In addition, the ideas of providing opportunities for small businesses and promoting a greater spread of ownership among different groups indicate its goal of favouring or protecting weak trading parties or certain groups of people. To achieve the aim of equity and fairness, South African competition law should be vigorously applied, but the existing substantive provisions may not fulfil the task of moving towards an equal and fair society. A comparative study of competition law may help to discover a proper model and a better solution for the problems of unequal economic power in South Africa.

Neuroimaging studies, such as the Human Connectome Project (HCP), often collect multifaceted data to study the human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our understanding of how different brain-related measures interact. In this study, we analyze the multi-block HCP data using data integration via analysis of subspaces (DIVAS). We integrate structural and functional brain connectivity, substance use, cognition, and genetics in an exhaustive five-block analysis. This gives rise to the important finding that genetics is the single data modality most predictive of brain connectivity, outside of brain connectivity itself. Nearly 14% of the variation in functional connectivity (FC) and roughly 12% of the variation in structural connectivity (SC) is attributed to shared spaces with genetics. Moreover, investigations of shared space loadings provide interpretable associations between particular brain regions and drivers of variability. Novel Jackstraw hypothesis tests are developed for the DIVAS framework to establish statistically significant loadings. For example, in the (FC, SC, and substance use) subspace, these novel hypothesis tests highlight largely negative functional and structural connections suggesting the brain’s role in physiological responses to increased substance use. Our findings are validated on genetically relevant subjects not studied in the main analysis.

Factor score indeterminacy is a characteristic property of factor analysis (FA) models. This research introduces a novel procedure, regression-based factor score exploration (RFE), which uniquely determines factor scores and simultaneously estimates other parameters of the FA model. RFE uniquely determines factor scores by minimizing a loss function that balances FA and multivariate regression, regulated by a tuning parameter. Theoretical aspects of RFE, including the uniqueness of factor scores, the relationship between observed and latent variables, and rotational indeterminacy, are examined. Additionally, clustering-based factor exploration (CFE) is presented as a variant of RFE, derived by generalizing the penalty term to enable the clustering of factor scores. It is demonstrated that CFE creates cluster structures more accurately than the existing method. A simulation study shows that the proposed procedures accurately recover true parameter matrices even in the presence of error-contaminated data, with lower computational demand compared to existing methods. Real data examples illustrate that the proposed procedures provide interpretable results, demonstrating high relevance to the factor scores obtained by existing methods.

We analyse seismic time series collected during experimental campaigns in the area of the David Glacier, Victoria Land, Antarctica, between 2003 and 2016. We observe hundreds of repeating seismic events, characterized by highly correlated waveforms (cross-correlation > 0.95), which mainly occur in the grounding zone, i.e. the region where the ice transitions from grounded ice sheet to freely floating ice shelf. The joint analysis of seismic events and observed local tidal measurements suggests that seismicity is not only triggered by a regular, periodic driver such as the ocean tides but also more likely by transient pulses. We consider potential environmental processes and their impact on the coupling between the glacier flow and the bedrock brittle failure. Among the environmental variables examined, our findings suggest that clustered and repeated seismic events may be related to transient episodes of ice-mass discharge correlated to a change in the subglacial hydrographic system that originates upstream of the glacier, lubricating the interface with the bedrock. This hypothesis is supported by the gravity variation observations provided by the GRACE satellite mission, which observed mass variations during periods characterized by seismic clustering.

We present a new corpus of child and child-directed speech (CDS) in Palestinian Arabic. It includes transcriptions following the CHILDES guidelines and features recordings of 16 monolingual Palestinian Arabic-speaking children with an age range of 19–58 months and their adult interlocutors. We analyse the children’s morphosyntactic development and identify a variety of target word orders (45 in child speech, 50 in CDS), with prevalent SV(O) structures; we also found high rates of null subjects in both populations, marginal errors in children’s verbal agreement morphology, and early emergence of serial verb constructions, observed from 23 months of age.

We construct moduli spaces of framed logarithmic connections and also moduli spaces of framed parabolic connections. It is shown that these moduli spaces possess a natural algebraic symplectic structure. We also give an upper bound of the transcendence degree of the algebra of regular functions on the moduli space of parabolic connections.