I present a new ontological argument that rests on two evaluative theses, both inspired by Anselm’s Proslogion 2. First, for any F and Q, it is no better for there to be an F, given Q, than it is for there to be something perfect. Second, it is better for there to be something perfect if there is such a thing than if there isn’t. It follows that there is something perfect. I examine these premises, consider some parodies, and suggest possible atheistic replies.

This article examines the replication of the Statue of Peace as a form of civic resistance and re-commemoration in response to the Japanese government’s efforts at de-commemoration. It advances three central arguments. First, replication functions as re-commemoration that resists state-led erasure of the “comfort women” memory. Second, this process constitutes a hauntological cycle, in which attempts to suppress unresolved memories only intensify their return. Third, both state and civic actors must embrace these haunting memories as enduring presences. The study draws on Derridean hauntology and case studies to support this framework.

In this study, we build on our previous work that examines the creative dancemaking collaboration between choreographers and arborist-dancers as they work together in rehearsals to create a performance featuring the workaday skillfulness of urban foresters. They face unique challenges and contingencies because to step into each other’s professional worlds requires a provisionally shared way of thinking that cuts across their diverse experiences; therefore, the two groups create external representations with their bodies (i.e., marking) to bridge epistemic divides. We extend our previous analysis of this microethnographic context—analyzing a routine where an arborist drives a loader truck to distribute mulch—by further demonstrating the semiotic purchase of Charles Goodwin’s interactional semiotics. Goodwin’s notion of “situated improvisation” is especially helpful for making sense of the embodied diagrams that emerge in marking together—a jointly crafted conceptual world makes perceptual experiences of both groups readable and deployable for dance creation.

Members of the majority party in Congress sometimes vote against bills that they prefer over the status quo. We estimate a model of congressional roll-call voting that allows for this kind of non-ideological protest voting. We find that protest voting has significant implications for roll-call-based estimates of ideology and other analyses that rely upon them. For example, a traditional item response theory model curiously identifies members of the Squad as relatively moderate Democrats, but our protest-voting-adjusted scores identify them as the most liberal members of Congress. We also find that previous studies may have underestimated responsiveness, the effects of ideology in elections, the utility of non-roll-call-based measures of ideology, and the increase in congressional polarization. Although the implications for most substantive applications are likely modest, our analyses suggest that future researchers can better measure legislative ideology by accounting for a small number of non-ideological votes.

NEURAL MATERIALS (2024) is a live AV show created by SONAMB (Vicky Clarke). The project represents a collaboration between Vicky Clarke, visual artist Sean Clarke, and industry partner Bela, a company specialising in hardware with interactive sensors for music-making. The AV show utilises a new performance system incorporating a hybrid set-up in combination with both a sound sculpture and the output of a machine learning model trained on a ‘post-industrial’ sonic dataset. The dataset renders in sound Manchester’s industrial past and present through field recordings of cotton mills, the canal network and the electromagnetic resonances of a newly gentrified city centre. This article analyses NEURAL MATERIALS as musical composition, live AV show and a demonstration of creative audio-generative AI, linking the work to scholarly and compositional legacies of Sonic Materialism and musique concrète. By combining documentation analysis and performance analysis, I interrogate how sound’s indexical properties are transformed via machine learning (ML) processes, questioning whether machines are able to evoke a sense of space or heritage. Ultimately, I contend that such audio-generative systems have the capacity to reshape our perception of industrial histories, technologies and future sonic realities, indexing sociohistorical cues that are reactivated at the point of listening.

Metaphors abound for mycorrhiza in both science and fiction. From the “wood wide web” to “mother trees,” “social networks” to “neurological networks,” analogies expand and transform public understanding of the complex and elusive interactions between plants and fungi occurring under our feet in forest ecosystems. However, the line between metaphor and the more-than-metaphorical, fact and fiction, is not always clear, causing heated debates about the role of metaphor in the scientific imagination and science communication. As a mycologist and literary scholar, we enact an interdisciplinary symbiosis inspired by mycorrhiza themselves to explore the mycorrhizal metaphors in the past decade, which are entangling and enriching both science and fiction, from Tade Thompson’s Rosewater (2016) to Merlin Sheldrake’s Entangled Life (2020), Richard Powers’s The Overstory (2018) to Suzanne Simard’s Finding the Mother Tree (2021). We reaffirm the fundamental value of metaphors in how scientists and nonscientists alike seek to understand fungi in a world increasingly fascinated by and dependent upon them.

In random-effects meta-analysis, the between-study heterogeneity variance, $\tau ^2$, is often reported but is not easy to interpret. For meta-analyses of differences (such as mean differences, standardized mean differences, or risk differences), the standard deviation (SD), $\tau $, indicates the extent to which studies’ true effects vary about their average. For meta-analyses of (natural) log-transformed measures of effect (such as log risk ratios [RRs]), we explain how the geometric SD, $\exp (\tau )$, is helpful to understand how untransformed measures (such as RRs) vary multiplicatively about their average. We recommend that authors and software developers report $\tau $ for differences and $\exp (\tau )$ for ratios, rather than $\tau ^2$. This will facilitate the interpretation of the magnitude of heterogeneity values, for example, the interpretation of heterogeneity estimates and confidence intervals beyond simple binary statements about the presence or absence of heterogeneity.

Stellarator boundary optimisation faces a fundamental numerical challenge: the extreme disparity between low- and high-mode amplitudes creates an optimisation landscape in which direct full-spectrum approaches typically converge to poor local minima. Traditionally, this challenge has been addressed through a computationally expensive, multi-step Fourier continuation, in which low Fourier modes are optimised first, followed by the gradual incorporation of higher modes. We present exponential spectral scaling (ESS), a technique that applies a mode-dependent exponential scaling factor to each Fourier mode. Our primary implementation uses the $L_{\infty }$ norm to determine the scaling pattern, creating a square spectral decay profile that effectively reduces the dynamic range of optimisation variables from 6–7 orders of magnitude to 2–3. This scaling aligns with the natural spectral decay of physically meaningful configurations and enables direct single-step optimisation using the full spectrum of boundary Fourier modes. ESS eliminates arbitrary staging decisions and reduces computation time by a factor of ${\sim}2{-}5$ in benchmark cases. In addition to accelerating optimisation, ESS improves robustness, reducing sensitivity to initial conditions and increasing confidence in avoiding local optima. We demonstrate the effectiveness of ESS across both quasi-axisymmetric (QA) and quasi-helically symmetric (QH) configurations, using two distinct optimisation toolkits: simsopt and desc.

For many years, political scientists have debated over voter competence in direct democracy. At the core of the discussion is whether this central institution enlightens citizens about political facts. However, scholars have primarily examined if direct democracy fosters general political knowledge even though referendums and ballot initiatives are policy-specific in nature, as citizens vote on particular political proposals. By utilising a range of unique panel survey data collected around four Danish European Union referendums, I show that voters’ knowledge of policy-specific information markedly increased during the campaigns. I also combine the survey data with an original media content analysis and find that the learning of issue-specific facts is more related to the opportunities provided by the media information environment than to individual ability or motivation. These results suggest that a broad group of voters acquire policy-specific facts that help them make informed choices when they are granted full control of political decision-making.

This manifesto was originally submitted into and went on to win the senior category in the 2025 Classical Association ‘Write | Speak | Design’ competition. Through a mix of research and my personal classics journey, it argues that the contemporary relevance and remote accessibility of classical subjects, along with the academic joy they encourage in learners, make the study of the ancient past as important as ever and highlights ways these areas could be harnessed to increase the importance of classics further.

This paper reviews research from the field of language teaching into post observation feedback i.e. the discussion that takes place after an observer has watched a pre-service or in-service teacher’s lesson. Post observation feedback is discussed with reference to four main themes: (1) perceptions of feedback; (2) reflection; (3) relationships (with two sub-themes of identity and facework); and (4) observer training. This review indicates that while the fields of language teaching and applied linguistics are leading research into post observation feedback, there remains important and interesting avenues for future research, which are discussed in this paper.

With the growing application of artificial intelligence (AI) and machine learning (ML), great potential exists to leverage these technologies in paleontology. Relative to many other scientific fields, a challenge of ML applied to paleontology is small sample sizes, particularly for fossil vertebrates. Shark teeth, abundant in the fossil record, provide a model system to use ML across varying sample sizes. Here we use six classes (taxa) of Neogene shark teeth for taxonomic identification, including a curated dataset of 3150 images. Each class was evaluated using an 80% training and 20% validation split, with a separate, external test set of 25 samples per class. Pretrained models perform well (accuracy > 90%), providing a strong baseline for classification. However, enabling fine-tuning of the ML model to identify fossil shark teeth improves performance considerably. Likewise, sample size per class also affects the accuracy of the models’ classifications. Smaller sample sizes (n = 50 individuals per class) yielded a mean accuracy of 93.4%, but plateaued at ~99% between 200 and 500 images per class. Confidence likewise increases with larger samples, from 81.8% (n = 50 individuals per class) to >90% (n = 300 to 500 individuals per class). Misidentifications followed consistent patterns, reflecting morphological similarities and/or poor preservation. Artificially increasing the training datasets using data augmentation improves the confidence of identifications. This research indicates that relatively small samples of vertebrate species (~50 to 500 individuals per class) can effectively train an ML model to identify these shark teeth with high levels of accuracy.