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Scientists as activists : An ethnography of the ‘critical moments’ in scientists’ transition to climate activism
Get PDFThis study presents the first ethnographic investigation of scientist climate activism, addressing a major gap in understanding how scientists navigate tensions between professional norms of neutrality, objectivity, and activism over time. Drawing on two years of immersive, longitudinal ethnography with Scientists for Extinction Rebellion in the UK, this study provides a rigorous, process-based account of how scientists enter activism, manage identity conflicts, and negotiate their boundaries of engagement. Findings show that identity-aligned spaces legitimise initial participation and foster belonging. Scientists strategically draw on professional expertise and scientific symbols (e.g., lab-coats, peer-reviewed papers) to legitimise action and engender collective identification. However, these same symbols can also limit participation to those who identify with them and generate expectations of universal expertise. Over time, activism reshapes professional identity, reinforcing moral conviction and producing hybrid scientist-activist identities. Sustained commitment depends on collective efficacy, peer affirmation, and care practices that support autonomy and buffer burnout. Escalation is non-linear: willingness to take risks increases with experience, yet professional, personal, and ethical considerations also influence decisions. By mapping critical moments in scientists’ activist trajectories, this study advances social psychological models of identity conflict by demonstrating how professional norms, moral commitments, and collective actions dynamically interact over time. It introduces the concept of hybrid scientist-activist identity formation as a process, providing original, rigorous, and significant insights that extend theory and inform strategies for effective scientist advocacy
In Emerald Fennel’s Wuthering Heights, domestic abuse has been recast as consensual kink
No full textExploring the Epistemic Grey Zone : New Research on Soldiering and Military Medical Ethics
No full textFibreCastML : an open web platform for predicting electrospun nanofibre diameter distributions for biomedical applications
No full textIntroduction Electrospinning is a scalable technique for generating fibrous scaffolds with tunable micro- and nanoscale architectures for tissue engineering, drug delivery, and wound care. Machine learning (ML) has emerged as a powerful tool to accelerate process optimisation; however, existing models typically predict only mean fibre diameters, overlooking the entire diameter distribution that governs scaffold functionality and biomimicry. This study introduces FibreCastML, the first open-access, distribution-aware ML framework that predicts full fibre diameter spectra from routinely reported processing parameters and provides interpretable insights into parameter influence. Methods A comprehensive meta-dataset of 68,538 fibre-diameter measurements from 1,778 studies across 16 biomedical polymers was curated. Six standard input parameters (solution concentration, voltage, flow rate, tip-to-collector distance, needle diameter, and rotation speed) were used to train 7 ML learners (linear model, elastic net, decision tree, multivariate adaptive regression splines, k-Nearest Neighbours, random forest, and radial-basis Support Vector Machine) under nested cross-validation with leave-one-study-out external folds to ensure generalisable performance. Model interpretability combined variable importance, SHapley Additive exPlanations (SHAP), correlation matrices, and 3D parameter maps. The FibreCastML web app integrates these capabilities with out-of-range detection, solvent suggestions, and automated Excel reports. Results Non-linear and local learners consistently outperformed linear baselines, achieving R 2 > 0.91 for polymers such as cellulose acetate, Nylon-6, Polyacrylonitrile, polyD,L-lactide, Polymethyl methacrylate, Polystyrene, Polyurethane, Polyvinyl alcohol and Polyvinylidene fluoride. Concentration emerged as the most influential variable globally. The FibreCastML app returns polymer-specific distribution plots, predicted-vs-observed diagnostics, feature importance and correlations, and transparent metrics ( R 2 , RMSE, mean absolute error) for user-defined settings. In an experimental validation case using different electrospinners and microscopies, predicted diameter distributions closely matched experimental measurements (Kolmogorov–Smirnov p > 0.13 and overlap coefficient of 84%). Discussion By shifting from mean-centric to distribution-aware modelling, this work establishes a new paradigm for electrospinning design. FibreCastML enables reproducible, sustainable, and data-driven optimisation of scaffold architecture, bridging experimental and computational domains. Openly available, it empowers laboratories worldwide to perform faster, greener, and more reproducible electrospinning research, advancing sustainable nanomanufacturing and biomedical innovation
Seasonal nitrogen enrichment alters plant community stability–area relationship through decreased biodiversity, species asynchrony, and population stability
Get PDFAtmospheric nitrogen (N) deposition generally reduces the temporal stability of plant communities (community stability). The positive community stability–area relationship (CSAR) has been reported, but the effects of N deposition on CSAR are unexplored, particularly given that plant N absorption rhythm links with seasonal N enrichment. By conducting an experiment with N additions during autumn, winter, or the growing season in a temperate grassland, we employed the first 6 years' nested plant survey over 0.01–16 m2 to explore the influence on CSAR. We found that community stability still increased with area under N addition. Seasonal N additions reduced community stability at the local scale (i.e. CSAR intercepts), while N addition in winter or the growing season, but not autumn, reduced CSAR slopes. Moreover, N additions altered the slopes of the relationships between species diversity, species asynchrony, and population stability and area, though the effects varied in magnitude among seasonal inputs. Partial regressions revealed that species diversity exerted stronger pure effects (average about four times in R2) on stability than area. This benefit was attributed to increased species asynchrony and population stability, even with N‐enriched conditions. Our research showed distinct degrees of influence of seasonal N addition on community stability across scales, highlighting that coupling seasonality and spatial scales is warranted for preserving biodiversity to maintain natural ecosystems under N deposition scenarios. Read the free Plain Language Summary for this article on the Journal blog
Reflecting on Sociological Research Online
No full textIn this article, I reflect on my history, relationship, and experiences with Sociological Research Online (SRO), its role in and influence on different aspects of my career, including being the journal where I published my first academic article and first served on an editorial board, as well as its importance within academic publishing and Sociology itself. The piece is divided into two sections representing different aspects of my connection: Research, Publishing, and Teaching and Editing and Engagement
Coincident ENA and narrowband radio emissions as diagnostics of inward plasma transport at Saturn
No full textSaturn’s kilometric radiation (SKR) and Energetic Neutral Atom (ENA) emissions are important diagnostics of the planet's magnetospheric dynamics, intensifying during global plasma injections and displaying characteristic planetary periodicity. Related to the SKR emissions are myriametric narrowband radio emissions that typically appear in the hours following SKR intensification. While these narrowband emissions (centered on frequencies around 5 and 20 kHz) have been associated with ENA signatures at evening local times (LT), the radial dependence of this relationship remains untested. Narrowband radio sources are thought to be triggered by temperature and density gradients at the inner edge of the Enceladus plasma torus. In this study, we use ENA keograms separated by radial distance (inner, middle, and outer magnetosphere) to quantify the timing of narrowband 'bursts' relative to ENA intensity. Additionally, we analyze both ENA and radio emissions in the co-rotating planetary frame to compare their rotational modulation. Our results show that 5 kHz bursts correlate most strongly with ENA enhancements rotating through the dusk-midnight sector in the inner magnetosphere. The 20 kHz band shows a weaker correlation and occurs slightly later. Specifically, 5 kHz bursts lead 20 kHz bursts by ~2–2.5 hours LT, consistent with a phase-locked relationship. These findings suggest that narrowband emission is triggered by a spatially dependent sequence: injected plasma drives the necessary anisotropy as it reaches the cooler inner magnetosphere beyond dusk. This provides a clearer picture of how global injection events influence Saturn’s internal plasma environment
Lunchbox AI Beta
No full textLunchbox AI is a web-based platform designed to support classroom-based GenAI exploration. Its design was informed by the insights and heuristics generated from the two studies, Ryelands AI Lab and The Interview Study. The beta version is now published for testing purposes, but unavailable to the public
Machine learning techniques based multi-parameter analysis and design of nonlinear helical structures considering internal structure collisions
No full textHelical structures are widely used in various engineering disciplines due to their energy storage and vibration damping characteristics. Existing studies have investigated the dynamic responses of various helical structures under static, low-speed dynamic and high-speed dynamic loadings. However, systematic understandings on the relationship between the complicated multi-parameters of helical structures and significant spike forces remain challenging. The present work develops a transient finite element model for a beehive helical structure sample, of which results are validated by a real engine test at 7800 rpm engine speed. The simulation results are in excellent agreement with the testing results and reveal that the dynamic spike forces are caused by collision effects between narrow coils. Next, 2 types of machine learning models, namely a deep neural networks (DNN) model and a genetic programming (GP) model, are developed and compared using simulation results. It is noted that the deep neural networks model has better prediction accuracy with the R² value of the testing set reaches 0.939. Finally, the relationships between multiple geometric parameters of the helical structure and the peak dynamic forces are plotted based on the developed machine learning model. In addition, design spaces that balance lower peak forces and vibration damping performance are proposed. These findings would be beneficial for systematic multi-parameter analysis and better designs of helical structures with superior mechanical performance