The crystal structure of palovarotene has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Palovarotene crystallizes in the space group P-1 (#2) with a = 10.2914(4), b = 11.8318(7), c = 11.9210(5) Å, α = 66.2327(11), β = 82.5032(9), γ = 65.3772(9)°, V = 1,206.442(28) Å3, and Z = 2 at 298 K. The crystal structure consists of chains of O–H···N hydrogen-bonded palovarotene molecules along the <0,−1,1 > axis; the graph set is C1,1(14). The powder pattern has been submitted to the International Centre for Diffraction Data® for inclusion in the Powder Diffraction File™ (PDF®).

This research report presents the development and validation of Auto Error Analyzer, a prototype web application designed to automate the calculation of accuracy and its related metrics for measuring second language (L2) production. Building on recent advancements in natural language processing (NLP) and artificial intelligence (AI), Auto Error Analyzer introduces an automated accuracy measurement component, bridging a gap in existing assessment tools, which traditionally require human judgment for accuracy evaluation. By utilizing a state-of-the-art generative AI model (Llama 3.3) for error detection, Auto Error Analyzer analyzes L2 texts efficiently and cost-effectively, producing accuracy metrics (e.g., errors per 100 words). Validation results demonstrate high agreement between the tool’s error counts and human rater judgments (r = .94), with microaverage precision and recall in error detection being high as well (.96 and .94 respectively, F1 = .95), and its T-unit and clause counts matched outputs from established tools like L2SCA. Developed under open science principles to ensure transparency and replicability, the tool aims to support researchers and educators while emphasizing the complementary role of human expertise in language assessment. The possibilities of Auto Error Analyzer for efficient and scalable error analysis, as well as its limitations in detecting context-dependent and first-language (L1)-influenced errors, are also discussed.

Regional governments are one of the largest but most understudied interest groups, employing a wide range of advocacy tactics like hiring professional lobbyists and face-to-face lobbying. However, we know little about why some succeed in influencing public policy while others do not. This gap arises because existing theories of interest groups and intergovernmental mobilization focus on resources—money and legitimacy—that regional governments typically lack control over. To address this, I propose a theoretical framework of intergovernmental lobbying success tailored to regional governments, emphasizing the convergence of five distinct conditions. Using new and original data on the 26 Swiss cantons’ influence on federal policy and employing set-theoretic methods (csQCA), I demonstrate that no single condition explains intergovernmental lobbying success. Instead, five causal pathways lead to a regional government shaping federal policy in line with its preferences. These findings have significant implications for understanding the effects of intergovernmental lobbying on representation, inequality, and unequal policy responsiveness, potentially contributing to rising political discontent, growing rural resentment, or citizen alienation.

We investigate galaxy groups that reside in the field but have been previously processed by galaxy clusters. Observationally, they would appear to have the same properties as regular field groups at first glance. However, one would expect to find quantifiable differences in processed groups as dynamical interactions within clusters perturb them. We use IllustrisTNG300 simulation to statistically quantify that processed groups of galaxies show different properties compared to regular field groups. Our analysis encompasses a broad range of groups with total masses between $8 \times 10^{11} \mathrm{ M}_{\odot}$ and $7 \times 10^{13} \mathrm{ M}_{\odot}$. We distinguish between processed groups that passed through a galaxy cluster and capture more galaxies, referred to as thief groups, and groups that did not capture any new members, referred to as non-thief groups. The employed statistical tools show that thief groups are generally less compact and contain more members, while non-thief groups seem to have the same properties as the field groups which makes them indistinguishable.

We examine how team allocation shapes mutual fund managers’ compensation as well as their future productivity and careers. Assignment to a high-quality team lowers immediate compensation but accelerates career development—sharpening investment skill, boosting media visibility, deepening industry and style specialization, and raising future revenue. Team quality also raises promotion odds and explains the steep, tenure-based earnings profile common in asset management. Team allocation therefore acts as a career-steering mechanism embedded in fund-family compensation contracts.

Language models have the ability to identify the characteristics of much shorter literary passages than was thought feasible with traditional stylometry. We evaluate authorship and genre detection for a new corpus of literary novels. We find that a range of LLMs are able to distinguish authorship and genre, but that different models do so in different ways. Some models rely more on memorization, while others make greater use of author or genre characteristics learned during fine-tuning. We additionally use three methods – direct syntactic ablation of input text and two means of studying internal model values – to probe one high-performing LLM for features that characterize styles. We find that authorial style is easier to characterize than genre-level style and is more impacted by minor syntactic decisions and contextual word usage. However, some traits like pronoun usage and word order prove significant for defining both kinds of literary style.

Comparing the performance of bilinguals to monolinguals can introduce bias in language assessment. One potential impact is misidentification of developmental language disorder (DLD). Nonlinguistic cognitive processing tasks may reduce assessment bias because they measure underlying DLD weaknesses without relying on linguistic stimuli. This study examined the extent to which nonlinguistic cognitive processing tasks showed bias, compared to a traditional language assessment, sentence repetition. Participants were 161 five-to-seven-year olds from diverse language backgrounds who completed nonlinguistic auditory and visual assessments of processing speed, sustained selective attention and working memory. We examined psychometric properties and performance on each task among bilingual and monolingual children. We also conducted bilingual-to-bilingual comparisons to examine performance differences by first-language typology and exposure amount. Results suggest minimal assessment bias in the nonlinguistic cognitive processing tasks, particularly in comparison to sentence repetition. Nonlinguistic cognitive processing tasks may ultimately contribute to less-biased identification of DLD in diverse populations.

Critical scholars and intellectuals are often viewed as vanguards of intellectual rigor, moral integrity, and left-leaning/left-liberal politics. In particular, their trajectories tend to be examined from a sympathetic lens: as supporters of lower-class social movements. Unfortunately, this approach overlooks the varied agency of these critical scholars and their complex relationship with the very movements that they often claim to represent. It obscures their potentially unequal socioeconomic status and cultural gap with the movements they engage with. This is not to dismiss their contribution or deny the reality of state repression against some of them, but a more grounded, sober approach to studying these cognitive workers is needed.

This study investigates the value-appropriating, politically-moderating, status-seeking tendency in some parts of critical knowledge production and activism. It advances several claims. First, the increasing neoliberalisation of the research sector exacerbates the process of class differentiation among critical scholars and intellectuals. The majority join the swelling rank of precarious cognitariat, whereas a selected stratum becomes part of the professional managerial class. Second, the latter stratum contains new intellectual actors who enjoy economic, cultural, and, political benefits from their advantaged position at the expense of precarious scholar-activists and marginalised communities, as exemplified in their public celebrity status or appointment into policymaking decisions. Lastly, as an illustration, and a form of self-criticism, I interrogate my position as an early-career researcher of Indonesian politics, show my own role and complicity in the neoliberal research industrial complex, and reflect on possible ways out of this politico-intellectual impasse.

Argumentation is often conceived as a rational response to disagreement, even when it does not resolve differences of opinion. Arguing in the face of disagreement has, however, distinctive epistemic effects. Sometimes argumentation achieves convergence of opinion or at least the mutual recognition that a more thorough inquiry is required. But facing disagreement, participants of argumentative exchanges quite often remain steadfast in their initial views or even radicalize them. Can we make sense of these latter situations? To account for their occurrence, it is common to point out that people’s ability to argue is flawed, that an “argumentative culture” is lacking, and that emotional and other non-rational factors often interfere in confrontative situations. But these suggestions do not amount to a thorough satisfactory explanation. In this paper, I provide the outline of a purely epistemic account of these peculiar effects of argumentation in the face of disagreement. I argue that probabilistic models of degrees of confidence (or “credences”) can shed light on the conditions that give rise to several of these effects. This could provide some guidance on how to avoid them.

This article examines the practice of post-mortem examination in the Royal Navy during the French Revolutionary and Napoleonic Wars (1793–1815). The professional medical logbooks kept by ship’s surgeons as part of their mandated practice reveal that they turned to pathological anatomy to diagnose their patients – a technique typically associated with French anatomy during this period. I show that these post-mortem dissections blended medicine and surgery together by correlating clinical signs and symptoms of disease with pathological manifestations of disease in the bodies after death. This article also considers the medical culture that existed on these ships that enabled this research, specifically how captains, officers and crew responded to, and interpreted, such medical enquiry on board. By resituating the naval ship as a site of medical experimentation and enquiry, I explore how naval surgeons participated in medical research within the Royal Navy and used the ship space to engage in pathological anatomy before their British civilian counterparts flocked to French hospitals after the wars.