Word frequency databases like SPALEX and SUBTLEX-ESP treat Spanish as a uniform language, but prior studies and an initial survey (Experiment 1) revealed significant lexical differences between Spanish in Spain and Latin American countries, especially Chile. To establish subjective frequencies of Spanish word usage, an extended survey (Experiment 2) was conducted with Chilean participants, categorizing words by usage area: General, Spain, Chile, and Latin America. Consistent with the initial survey, Chilean participants assigned subjective higher ratings to General and Chilean words. In a lexical decision experiment (Experiment 3), participants responded faster and more accurately to words from these categories. Using survey data, simulations with Multilink+ (Experiment 4) revealed that subjective word ratings better predicted Chilean reaction times than frequencies from existing databases. These findings emphasize the need to address Spanish dialectal differences in research, with word ratings offering a more accurate measure of region-specific lexical nuances than current databases.

Random-effects meta-analysis is a widely applied methodology to synthesize research findings of studies related to a specific scientific question. Besides estimating the mean effect, an important aim of the meta-analysis is to summarize the heterogeneity, that is, the variation in the underlying effects caused by the differences in study circumstances. The prediction interval is frequently used for this purpose: a 95% prediction interval contains the true effect of a similar new study in 95% of the cases when it is constructed, or in other words, it covers 95% of the true effects distribution on average in repeated sampling. In this article, after providing a clear mathematical background, we present an extensive simulation investigating the performance of all frequentist prediction interval methods published to date. The work focuses on the distribution of the coverage probabilities and how these distributions change depending on the amount of heterogeneity and the number of involved studies. Although the single requirement that a prediction interval has to fulfill is to keep a nominal coverage probability on average, we demonstrate why the distribution of coverages should not be disregarded. We show that for meta-analyses with small number of studies, this distribution has an unideal, asymmetric shape. We argue that assessing only the mean coverage can easily lead to misunderstanding and misinterpretation. The length of the intervals and the robustness of the methods concerning the non-normality of the true effects are also investigated.

Increasingly, simulation-based teaching and learning is finding a place within politics and international relations (IR) programmes. The majority of literature on this style of teaching and learning has positioned it as both an aid to content delivery and as a response to the many challenges facing contemporary higher education. Little guidance is given, however, to the practical considerations of using simulations as a component of assessment or as informing assessed tasks. This article draws upon the experience of the authors in adapting the well-established Model United Nations (MUN) simulation programme for delivery as an assessed module at a British university. This has involved balancing institutional teaching, assessment and validation requirements with the successful simulation of diplomatic practice. The article introduces the MUN simulation and explores the extant pedagogic literature encouraging the use of simulation-based learning in IR curricula, before moving on to provide an overview of the rationale for the various decisions the authors have made in adapting the simulation for delivery as an assessed curriculum component. The article asserts the value of introducing assessed simulations within IR coursework and provides guidance on how student performance in pedagogic simulations might best be assessed.

Collaboration and its promotion by funders continue to accelerate. Although research has identified significant transaction costs associated with collaboration, little empirical work has examined the broader, societal-level economic outcomes of a resource-sharing environment. Does an environment that encourages collaboration shift our focus toward certain types of social objectives and away from others? This paper uses agent-based Monte Carlo simulation to demonstrate that collaboration is particularly useful when resources are rare but a social objective is commonly held. However, collaboration can lead to bad outcomes when the objective is not commonly shared; in such cases, markets outperform collaborative arrangements. These findings suggest that encouraging a resource-sharing environment can lead to inefficiencies even worse than market failure. We also demonstrate that failure to account for transaction costs when prescribing collaboration can result in quantifiably lower outcome levels than expected.

In recent years, a growing body of literature has widely investigated the impact of role-playing simulations in teaching politics and international relations. While scholars agree that participating in simulations is helpful for the students in developing their skills, the evidence about benefits is more mixed. Moreover, the question whether all students—regardless of their demographic or academic background—benefit similarly from simulations remains largely unanswered. This article, based on a cross-national survey submitted to students from Italy and the Netherlands who have participated in the Model United Nations (MUN), provides an innovative contribution to the current literature by looking at views and opinions of students coming from different educational contexts. Our empirical results suggest that students perceive that MUN increases their skills regardless of their academic and socio-demographic background. The quantitative analysis, based on OLS regression models, reveals that the individual students’ background does not influence their perceived benefit, nor their enjoyment of the experience. MUNs appear to be educational as well as fun for all students, regardless of their age, gender, field of study, seniority, and academic homeland.

Recent literature suggests that undergraduate students in political science dread studying methods, especially quantitative methods. Nonetheless, we believe that teaching students the value of quantitative analysis is critical. Going beyond traditional teaching approaches, we use a simulation to conceptualize the logic and process of quantitative analysis. In our simulation, student participants are told they are in a prison with another prisoner, where both have the objective to make the other prisoner crossover, using any strategy. The simulation allows students to generate and test basic hypothesis about students’ characteristics and their strategies, as well as operationalizing variables and quantifying results.

The benefits of simulation exercises easily outweigh potential weaknesses, and most of these weaknesses can be addressed by careful preparation. This article seeks to encourage instructors in higher education to embrace simulations as a means of encouraging active learning and greater retention as well as improving student and teacher satisfaction. However, there is not to date much helpful guidance, for first-time appliers, as to how to set up simulations. This contribution seeks to contribute to closing that gap by reflecting on the experience of two EU Council simulations that the author has organised. The aim is to openly review things that worked well and things that did not so as to allow colleagues interested in engaging in simulations in the future to see the reasons behind certain choices and perhaps avoid weaknesses of simulations set up by ‘freshers’. In this context, articles are all too often presented as success stories, hiding errors or adaptations in the process, whereas in fact much can be learned from publically exposing and reflecting upon shortcomings and weaknesses of research and teaching design and processes. To finish up, some tips for ‘freshers’ have been compiled.

The article addresses the paradox of the Russian legislation on nonterritorial, aka “national-cultural” autonomy – the lack of utilitarian ends and functions combined with a high domestic public demand for it. The author seeks to explain the case as simulation, or activities for the sake of demonstrating activities without definite substantive purposes. The analysis reveals that the relevant law’s goals and justifications voiced by the stakeholders were merely a combination of socially acceptable opinions unrelated to result-oriented action. These opinions were part of a common-sense worldview based on group-centric and essentialist vision of ethnicity and on neoliberal postulates, such as the need to foster bottom-up initiative and self-organization, the rejection of governmental social obligations, and the need for strict regulatory mechanisms securing fair relationships among the players. A brief comparison with a similar case in Europe reveals that simulation can take place in other contexts related to nonterritorial autonomy. Thus, a focus on simulative action must be a promising approach for research concerning the imaginaries of groups as entities and actors.

The circular economy (CE) seeks to replace traditional linear models by focusing on resource reuse and circulation. However, developing effective CE business strategies is difficult due to complex user behaviors and product flows. Existing scenario analysis tools often rely on survey-based conjoint methods, raising concerns about discrepancies with real purchasing patterns. This study introduces a data-driven simulation approach that employs a consumer preference model and product circulation processes based on actual operational data. Applied to a second-hand PC rental business, our method more accurately reproduces market behavior and reveals that targeting certain customer segments can enhance profitability and resource utilization. These findings underscore the approach’s value as a practical tool for pre-evaluating strategies in CE businesses.

Variation simulation approaches are frequently used to analyse the effects of geometrical variations on the final product quality. Various software tools are used during product development as they strongly differ in their specified goals, the context of use, and users. Although a few workarounds and information-sharing strategies exist, switching software usually results in the simulation model being built from scratch, leading to redundant manual effort and uncertainties. This paper examines the potential and limitations of the Quality Information Framework (QIF) information model in improving collaborative work within a heterogeneous simulation software landscape by exchanging variation simulation model-related information in a standardised Model-Based Definition sense. An application scenario shows how QIF can bridge the gap between tools used in early and late design phases.

This research examines, during the human-AI interaction process, how generative AI’s depiction of human bodies reflects and perpetuates able-bodied norms, positioning disabled or grotesque bodies as “errors.” Through a feminist and disability studies lens and employing archival research and visual analysis, this research challenges traditional notions of bodily normativity, advocating for inclusivity in AI-generated imagery. It underscores how labeling nonconformity as an error perpetuates able-bodied standards while erasing the visibility and autonomy of disabled bodies. By critiquing generative AI’s role in reinforcing societal norms, this study calls for reimagining human-AI interactions with a shift in perception and advocates for an approach that neither devalues nor excludes disabled bodies.

Food production systems are shaped by external factors, such as social events and economic shifts, which influence and are influenced by labour dynamics—e.g., workforce availability—and human factors—e.g., worker skills. Using a systems approach, this paper explores how labour shortages impacting worker teams—such as in terms of mixture of availability, skills, and human behaviours—affect production and quality. UK apple harvesting is chosen as a case study due to its reliance on skilled seasonal migrant workers. Findings highlight the need for strategies such as upskilling local workers, enhancing training programmes, and adopting new technologies to mitigate labour shortages and enable high-performance collaborative worker groups.

Lightweight design is critical for improving the efficiency and sustainability of engineering applications. Laminated composites, with their high strength-to-weight ratio and tailored material properties, play a key role but introduce interlaminar stresses, particularly near free edges where delamination failures often occur. Addressing these stresses typically requires computationally expensive 3D finite element simulations, limiting their use in early design stages. This study presents a machine learning approach using Gaussian process regression and artificial neural networks to efficiently predict interlaminar stresses based on in-plane stress data from shell FE simulations. Achieving high predictive accuracy, this method enables cost-effective, early-stage composite design optimization under complex loading scenarios.