The year 2025 marks a watershed moment for Politics of the Low Countries (PLC). From 1 January 2025, PLC transitioned from Boom/Eleven Publishers to Radboud University Press (RUP) as a diamond open access (DOA) journal, making all articles free to publish and free to read. This editorial outlines the rationale behind PLC's decision to adopt a DOA publishing model, examines the implications of this transition for the journal's financial structure and editorial workflow, and demonstrates how researchers in Belgium, the Netherlands, and Luxembourg will benefit from this change.
Online political divisions, such as fragmentation or polarization, are a growing global concern that can foster radicalization and hinder democratic cooperation; however, not all divisions are detrimental, some reflect pluralism and healthy diversity of opinion in a democracy. While prior research has predominantly focused on polarization in the United States, there remains a limited body of research on political divides in multiparty systems, and no universal method for comparing fragmentation across countries. Moreover, cross-country comparison is rare. This study first develops a novel measure of structural political fragmentation built on multi-scale community detection and the effective branching factor. Using a dataset of 18,325 political influencers from Brazil, Spain, and the United States, we assess online fragmentation in their Twitter/X co-following networks. We compare the fragmentation of the three countries, as well as the ideological groups within each. We further investigate factors associated with the level of fragmentation in each country. We find that political fragmentation differs across countries and is asymmetric between ideological groups. Brazil is the most fragmented, with higher fragmentation among the left-wing group, while Spain and the United States exhibit similar overall levels, with the left more fragmented in Spain and the right more fragmented in the United States. Additionally, we find that social identity plays a central role in political fragmentation. A strong alignment between ideological and social identities, with minimal overlap between ideologies, tends to promote greater integration and reduce fragmentation. Our findings provide explanations for cross-national and ideological differences in political fragmentation.
Is lowering the voting age to 16 years a genuine breakthrough in reigniting the youth’s enthusiasm for traditional politics in the long run? This remains a matter of uncertainty and, as such, forms the central inquiry of this exploration into the potential implications of extending voting rights to non-adults for Belgium and the Netherlands. The democratic landscape of the Low Countries stands at a transformative juncture, with Belgium marking a historic milestone by granting voting rights to non-adults for the first time in its political history.
The sustainability of the academic ecosystem relies on researcher demographics and gender balance, yet assessing these dynamics in a timely manner for policy is challenging. Here, we propose a researcher population pyramid framework for tracking demographic and gender trajectories across countries using publication data. We provide a timely snapshot of historical and present demographics and gender balance across 58 countries, revealing three contrasting patterns among research systems: Emerging systems (e.g., Arab countries) exhibit high researcher inflows with widening gender gaps in cumulative productivity; Mature systems (e.g., the United States) show modest inflows with narrowing gender gaps; and Rigid systems (e.g., Japan) lag in both. Furthermore, by simulating future scenarios, the framework makes potential trajectories visible. If 2023 demographic patterns persist, Arab countries' systems could resemble mature or even rigid ones by 2050. Our framework provides a robust diagnostic tool for policymakers worldwide to foster sustainable talent pipelines and gender equality in academia.
Accurate fertility estimates at fine spatial resolution are essential for localized public health planning, particularly in low- and middle-income countries (LMICs). While national-level indicators such as age-specific fertility rates (ASFR) and total fertility rate (TFR) are often reported through official statistics, they lack the spatial granularity needed to guide targeted interventions. To address this, we develop a framework for subnational fertility estimation using small-area estimation (SAE) techniques applied to birth history data from household surveys, in particular Demographic and Health Surveys (DHS). Disaggregation by geographic area, time period, and maternal age group leads to significant data sparsity, limiting the reliability of direct estimates at fine scales. To overcome this, we propose a suite of methods, including direct estimators, area-level and unit-level Bayesian hierarchical models, to produce accurate estimates across varying spatial resolutions. The model-based approaches incorporate spatiotemporal smoothing and integrate covariates such as maternal education, contraceptive use and urbanicity. Using data from the 2021 Madagascar DHS, we generate district-level ASFR and TFR estimates and evaluate model performance through cross-validation.
The empirical literature provides mixed results on the relationship between inflation and unemployment, therefore, there is no consensus on validity and stability of the Phillips Curve. It also seems to be closely related with country-specific factors and the examination time periods. Considering the importance of this trade-off for policy-makers, this study aims to examine validity and stability of expectations-augmented Phillips Curve across 41 countries focusing on three different time periods between 1980 and 2016. The study documents several findings both in country-specific and in panel estimation analysis. First, we find that forward-looking characteristic of inflation picks up weight after 1990's which indicates that inflation became more sensitive to the expected prices. Second, we observe that inflation in developed markets is more forward-looking comparing to emerging and frontier markets. This indicates that developed markets dear forward-looking price expectations more than other markets. Third, we find that that both forward- and backward-looking Phillips Curve fails to work in Brazil, Greece, Indonesia, Mexico, South Africa, Romania, and Turkey. We address it to their long history of high and volatile inflation.
Rachael Colley, David Manlove, Daniel Paulusma
et al.
Kidney Exchange Programmes (KEPs) facilitate the exchange of kidneys, and larger pools of recipient-donor pairs tend to yield proportionally more transplants, leading to the proposal of international KEPs (IKEPs). However, as studied by \citet{mincu2021ip}, practical limitations must be considered in IKEPs to ensure that countries remain willing to participate. Thus, we study IKEPs with country-specific parameters, represented by a tuple $Γ$, restricting the selected transplants to be feasible for the countries to conduct, e.g., imposing an upper limit on the number of consecutive exchanges within a country's borders. We provide a complete complexity dichotomy for the problem of finding a feasible (according to the constraints given by $Γ$) cycle packing with the maximum number of transplants, for every possible $Γ$. We also study the potential for countries to misreport their parameters to increase their allocation. As manipulation can harm the total number of transplants, we propose a novel individually rational and incentive compatible mechanism $\mathcal{M}_{\text{order}}$. We first give a theoretical approximation ratio for $\mathcal{M}_{\text{order}}$ in terms of the number of transplants, and show that the approximation ratio of $\mathcal{M}_{\text{order}}$ is asymptotically optimal. We then use simulations which suggest that, in practice, the performance of $\mathcal{M}_{\text{order}}$ is significantly better than this worst-case ratio.
We investigate spatial dependence in Zipf's law for cities among the OECD countries. The aim is to identify an upper tail of the distribution that follows a power law (Pareto) but is perturbed by spatial autocorrelation, as indicated by a coefficient with a significant minor or major deviation from a distribution corresponding to a (non-spatial) Zipf law. For that purpose, we augment the usual Pareto model with a spatial weight matrix and apply SEM/SAR regressions. The results for the OECD countries are mixed. In particular, we investigate the rank-size distribution of cities by estimating local Moran-I coefficients (LISA) along the city ranks to locate the causes of spatial dependence. As an example, we demonstrate the approach for Belgium, a medium-sized OECD country.
The article discusses the use of development assistance as a foreign policy tool by the Benelux countries, namely Belgium, the Netherlands and Luxembourg. The paper aims to answer the question whether the development assistance provided by the Benelux states corresponds to their political statements which cite this policy area as a soft power instrument, or the three approaches to development assistance rather reflect other goals, e.g. beneficial economic cooperation with developing countries. The article analyzes international statistical data, sociological surveys and official development assistance-related documents, using qualitative historical and descriptive as well as genealogical and historical methods to trace the evolution of the three countries’ approaches. The concepts of small and middle powers, to which the Benelux countries belong, and the concept of soft power constitute the theoretical and methodological framework of the research. The article concludes that the Benelux countries’ approaches to development assistance are different, complex and changeable. The pursuit of economic goals helps explain at least some of the three states’ geographic and functional priorities, which translate into defining countries where Benelux’ companies are situated as key partners or specializing in the development assistance areas where these companies can be involved. This pursuit is most clearly evident in the Dutch approach, while Luxembourg seems to value soft power function more as it enhances the country’s image as one of the most generous donors and a responsible member of the international community. Unlike Luxembourg, the Netherlands and Belgium show a downward trend in the amount of assistance allocated (with the exception of 2015 and 2022), amid doubts about the effectiveness of development assistance and securitization of this area; however, the ongoing public debates keep relevant the use of development assistance as soft power vis-à-vis the countries of the Global South.
Knowing which countries contribute the most to pushing the boundaries of knowledge in science and technology has social and political importance. However, common citation metrics do not adequately measure this contribution. This measure requires more stringent metrics appropriate for the highly influential breakthrough papers that push the boundaries of knowledge, which are very highly cited but very rare. Here I used the recently described Rk index, specifically designed to address this issue. I applied this index to 25 countries and the EU across 10 key research topics, five technological and five biomedical, studying domestic and international collaborative papers independently. In technological topics, the Rk indices of domestic papers show that overall, the USA, China, and the EU are leaders; other countries are clearly behind. The USA is notably ahead of China, and the EU is far behind China. The same approach to biomedical topics shows an overwhelming dominance of the USA and that the EU is ahead of China. The analysis of internationally collaborative papers further demonstrates the US dominance. These results conflict with current country rankings based on less stringent indicators.
Climate resilience across sectors varies significantly in low-income countries (LICs), with agriculture being the most vulnerable to climate change. Existing studies typically focus on individual countries, offering limited insights into broader cross-country patterns of adaptation and vulnerability. This paper addresses these gaps by introducing a framework for cross-country comparative analysis of sectoral climate resilience using meta-analysis and cross-country panel data techniques. The study identifies shared vulnerabilities and adaptation strategies across LICs, enabling more effective policy design. Additionally, a novel localized climate-agriculture mapping technique is developed, integrating sparse agricultural data with high-resolution satellite imagery to generate fine-grained maps of agricultural productivity under climate stress. Spatial interpolation methods, such as kriging, are used to address data gaps, providing detailed insights into regional agricultural productivity and resilience. The findings offer policymakers tools to prioritize climate adaptation efforts and optimize resource allocation both regionally and nationally.
The review article aims to provide an overview of the challenges and strategies for enhancing interoperability among health information systems in low- and middle- income countries (LMICs). Achieving interoperability in LMICs presents unique challenges due to various factors, such as limited resources, fragmented health information systems, and diverse health IT infrastructure. The methodology involves conducting a comprehensive literature review, synthesising findings, identifying challenges and strategies, analysing and interpreting results, and writing and finalising the article. The article highlights that the interoperability challenges include a lack of standardisation, fragmented systems, limited resources, and data privacy concerns. The article proposes strategies to enhance interoperability in LMICs, such as standardisation of data formats and protocols, consolidation of health information systems, investment in health IT infrastructure, and capacity building of health IT professionals in LMICs. The article aims to provide insights into the current state and potential strategies for enhancing interoperability among health information systems in LMICs, intending to improve healthcare delivery and outcomes in these KEYWORDS Interoperability, Health information systems, low and middle-income countries (LMICs), challenges, strategies, standardisation
Larry S. Liebovitch, William Powers, Lin Shi
et al.
Language is both a cause and a consequence of the social processes that lead to conflict or peace. Hate speech can mobilize violence and destruction. What are the characteristics of peace speech that reflect and support the social processes that maintain peace? This study used existing peace indices, machine learning, and on-line, news media sources to identify the words most associated with lower-peace versus higher-peace countries. As each peace index measures different social properties, there is little consensus on the numerical values of these indices. There is however greater consensus with these indices for the countries that are at the extremes of lower-peace and higher-peace. Therefore, a data driven approach was used to find the words most important in distinguishing lower-peace and higher-peace countries. Rather than assuming a theoretical framework that predicts which words are more likely in lower-peace and higher-peace countries, and then searching for those words in news media, in this study, natural language processing and machine learning were used to identify the words that most accurately classified a country as lower-peace or higher-peace. Once the machine learning model was trained on the word frequencies from the extreme lower-peace and higher-peace countries, that model was also used to compute a quantitative peace index for these and other intermediate-peace countries. The model successfully yielded a quantitative peace index for intermediate-peace countries that was in between that of the lower-peace and higher-peace, even though they were not in the training set. This study demonstrates how natural language processing and machine learning can help to generate new quantitative measures of social systems, which in this study, were linguistic differences resulting in a quantitative index of peace for countries at different levels of peacefulness.
Lakmal Meegahapola, William Droz, Peter Kun
et al.
Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies of models using different sensing modalities and machine learning techniques, with datasets collected in homogeneous populations. In contrast, less attention has been given to studying the performance of mood inference models to assess whether models generalize to new countries. In this study, we collected a mobile sensing dataset with 329K self-reports from 678 participants in eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical diversity on mood inference models. We define and evaluate country-specific (trained and tested within a country), continent-specific (trained and tested within a continent), country-agnostic (tested on a country not seen on training data), and multi-country (trained and tested with multiple countries) approaches trained on sensor data for two mood inference tasks with population-level (non-personalized) and hybrid (partially personalized) models. We show that partially personalized country-specific models perform the best yielding area under the receiver operating characteristic curve (AUROC) scores of the range 0.78-0.98 for two-class (negative vs. positive valence) and 0.76-0.94 for three-class (negative vs. neutral vs. positive valence) inference. Overall, we uncover generalization issues of mood inference models to new countries and how the geographical similarity of countries might impact mood inference.
Carla Sendra-Balcells, Víctor M. Campello, Jordina Torrents-Barrena
et al.
Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with limited access to high-end ultrasound equipment and data. This work investigates different strategies to reduce the domain-shift effect for a fetal plane classification model trained on a high-resource clinical centre and transferred to a new low-resource centre. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to $0.92\pm0.04$ and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources.
Anticipating the quantity of new associated or affirmed cases with novel coronavirus ailment 2019 (COVID-19) is critical in the counteraction and control of the COVID-19 flare-up. The new associated cases with COVID-19 information were gathered from 20 January 2020 to 21 July 2020. We filtered out the countries which are converging and used those for training the network. We utilized the SARIMAX, Linear regression model to anticipate new suspected COVID-19 cases for the countries which did not converge yet. We predict the curve of non-converged countries with the help of proposed Statistical SARIMAX model (SSM). We present new information investigation-based forecast results that can assist governments with planning their future activities and help clinical administrations to be more ready for what's to come. Our framework can foresee peak corona cases with an R-Squared value of 0.986 utilizing linear regression and fall of this pandemic at various levels for countries like India, US, and Brazil. We found that considering more countries for training degrades the prediction process as constraints vary from nation to nation. Thus, we expect that the outcomes referenced in this work will help individuals to better understand the possibilities of this pandemic.
Giacomo Barzon, Karan Kabbur Hanumanthappa Manjunatha, Wolfgang Rugel
et al.
By characterising the time evolution of COVID-19 in term of its "velocity" (log of the new cases per day) and its rate of variation, or "acceleration", we show that in many countries there has been a deceleration even before lockdowns were issued. This feature, possibly due to the increase of social awareness, can be rationalised by a susceptible-hidden-infected-recovered (SHIR) model introduced by Barnes, in which a hidden (isolated from the virus) compartment $H$ is gradually populated by susceptible people, thus reducing the effectiveness of the virus spreading. By introducing a partial hiding mechanism, for instance due to the impossibility for a fraction of the population to enter the hidden state, we obtain a model that, although still sufficiently simple, faithfully reproduces the different deceleration trends observed in several major countries.
This article is part of the forum 'Rethinking the VOC: Between Archival Management and Research Practice'.
In this essay, Jos Gommans provides a short survey of some recent developments in the historiography of the voc. He argues that Asian historians in particular have used the voc archive to acquire new insights into the regional histories of Asia. This progress needs to be consolidated by combining the further exploration of the voc-archive with the in-depth study of the Asian cultures that the voc encountered. Combining archival study and cultural empathy will also shed new light on the historical process of identity formation of both Asians and Dutch.
In dit essay geeft Jos Gommans een kort overzicht van enkele recente ontwikkelingen in de geschiedschrijving van de voc. Hij betoogt dat vooral Aziatische historici het voc-archief hebben gebruikt om nieuwe inzichten te verwerven in de Aziatische regionale geschiedenis. Deze vooruitgang moet worden bestendigd door de verdere exploratie van het voc-archief te combineren met een diepgaande studie van de Aziatische culturen waar de voc mee van doen had. Een dergelijke combinatie van archiefstudie en culturele empathie zal ook meer licht werpen op het historische proces van identiteitsvorming van zowel Aziaten alsook Nederlanders.