Netherlandish women writing literary texts in the period 1550-1830 are well-studied. The aim of this essay is to show how existing scholarship creates a tension between exceptionalism and marginalisation: scholarship primarily focuses on the challenges female authors faced because of their sex and does so by studying sources surrounding literary publications (such as journals, preliminaries, and portraits), with the notable exception of the works of some canonised women writers who are regarded as exceptions to the rule. As such, early modern Netherlandish women writers are primarily represented as a homogeneous group in scholarship. Yet, we argue, the diversity of literature (co-)authored by women, as well as the heterogeneous identities of these female authors themselves, invites us to destabilise the idea of ‘the female author’ in at least two interrelated ways: by linking their gender to other factors such as age, health, or race in women’s writing; and by approaching female-authored work as ‘collaborative’, i.e., reflecting different voices and hands, enabling scholars to view women in sometimes understudied roles and sources (such as translators and manuscripts, respectively). The future success of these proposed approaches depends on necessary infrastructural steps, as the digital availability and searchability of female-authored texts from the Low Countries is currently lagging behind that which is necessary.
Large Language Models (LLMs) often exhibit cultural biases due to training data dominated by high-resource languages like English and Chinese. This poses challenges for accurately representing and evaluating diverse cultural contexts, particularly in low-resource language settings. To address this, we introduce MyCulture, a benchmark designed to comprehensively evaluate LLMs on Malaysian culture across six pillars: arts, attire, customs, entertainment, food, and religion presented in Bahasa Melayu. Unlike conventional benchmarks, MyCulture employs a novel open-ended multiple-choice question format without predefined options, thereby reducing guessing and mitigating format bias. We provide a theoretical justification for the effectiveness of this open-ended structure in improving both fairness and discriminative power. Furthermore, we analyze structural bias by comparing model performance on structured versus free-form outputs, and assess language bias through multilingual prompt variations. Our evaluation across a range of regional and international LLMs reveals significant disparities in cultural comprehension, highlighting the urgent need for culturally grounded and linguistically inclusive benchmarks in the development and assessment of LLMs.
Toxic and uncivil politics is widely seen as a growing threat to democratic values and governance, yet our understanding of the drivers and evolution of political incivility remains limited. Leveraging a novel dataset of nearly 18 million Twitter messages from parliamentarians in 17 countries over five years, this paper systematically investigates whether politics internationally is becoming more uncivil, and what are the determinants of political incivility. Our analysis reveals a marked increase in toxic discourse among political elites, and that it is associated to radical-right parties and parties in opposition. Toxicity diminished markedly during the early phase of the COVID-19 pandemic and, surprisingly, during election campaigns. Furthermore, our results indicate that posts relating to ``culture war'' topics, such as migration and LGBTQ+ rights, are substantially more toxic than debates focused on welfare or economic issues. These findings underscore a troubling shift in international democracies toward an erosion of constructive democratic dialogue.
Paolo Canofari, Alessandro Piergallini, Marco Tedeschi
Do governments adjust budgetary policy to rising public debt, precluding fiscal unsustainability? Using budget data for 52 industrial and emerging economies since 1990, we apply panel methods accounting for cross-sectional dependence and heterogeneous fiscal conduct. We find that a primary-balance rule with tax-smoothing motives and responsiveness to debt has robust explanatory power in describing fiscal behavior. Controlling for temporary output, temporary spending, and the current account balance, a 10-percentage-point increase in the debt-to-GDP ratio raises the long-run primary surplus-to-GDP ratio by 0.5 percentage points on average. Corrective adjustments hold across high- and low-debt countries and across industrial and emerging economies. Our results imply many governments pursue Ricardian policy designs, avoiding Ponzi-type financing.
This article investigates the role of (spelled) language sounds in early modern Dutch theatre from 1570 until 1800. Using a self-designed computer tool, patterns of spelled language sounds in 167 theatre texts were analysed to investigate how sound patterns can distinguish plays from each other. The findings show that language sounds can characterise specific early modern plays in terms of period, genre, metre and authors. Next, a small sub study demonstrates that language sounds also have a predictive value. Further research with larger datasets can provide us with more insight into linguistic and cultural developments in the Dutch Republic.
Series Digital History
This article is part of a series on digital history in the Netherlands and Belgium. Eleven years after the publication of the widely-read BMGN-issue on digital history in 2013, this series aims to provide a new state of the field. It comprises four serially published articles, which collectively emphasise the diversity of researchers, questions, methods and techniques that define digital history in 2024. The articles are published online in a new, HTML-based format that better showcases the methods and visualisations of the research published here. Please scan the QR-code to navigate to the HTML-version of this article.
Hans Bekaert, Wim Van Dooren, Hans Van Winckel
et al.
In the context of the European Erasmus+ project Teaching ASTronomy at the Educational level (TASTE), we investigated the extent to which a learning module at school and a set of activities during a planetarium visit help students to gain insight in the Apparent Motion of the Sun and Stars. Therefore, we have set up a two treatment study with a pretest posttest design. In the four participating countries (Belgium, Germany, Greece and Italy), secondary school students studied the concept of the celestial globe at school using newly designed learning materials. By using a latent class analysis, we identified different classes of student answers on the AMoSS test. We show how students evolve from one class to another between pretest and posttest. Overall the results of the pretest and posttest show that a good understanding of the different aspects of the apparent motion of celestial bodies is difficult to achieve.
Legal documents are indispensable in every country for legal practices and serve as the primary source of information regarding previous cases and employed statutes. In today's world, with an increasing number of judicial cases, it is crucial to systematically categorize past cases into subgroups, which can then be utilized for upcoming cases and practices. Our primary focus in this endeavor was to annotate cases using topic modeling algorithms such as Latent Dirichlet Allocation, Non-Negative Matrix Factorization, and Bertopic for a collection of lengthy legal documents from India and the UK. This step is crucial for distinguishing the generated labels between the two countries, highlighting the differences in the types of cases that arise in each jurisdiction. Furthermore, an analysis of the timeline of cases from India was conducted to discern the evolution of dominant topics over the years.
This paper proposes a structural econometric approach to estimating the basic reproduction number ($\mathcal{R}_{0}$) of Covid-19. This approach identifies $\mathcal{R}_{0}$ in a panel regression model by filtering out the effects of mitigating factors on disease diffusion and is easy to implement. We apply the method to data from 48 contiguous U.S. states and a diverse set of countries. Our results reveal a notable concentration of $\mathcal{R}_{0}$ estimates with an average value of 4.5. Through a counterfactual analysis, we highlight a significant underestimation of the $\mathcal{R}_{0}$ when mitigating factors are not appropriately accounted for.
Raffaele Sommese, Roland van Rijswijk-Deij, Mattijs Jonker
Domain lists are a key ingredient for representative censuses of the Web. Unfortunately, such censuses typically lack a view on domains under country-code top-level domains (ccTLDs). This introduces unwanted bias: many countries have a rich local Web that remains hidden if their ccTLDs are not considered. The reason ccTLDs are rarely considered is that gaining access -- if possible at all -- is often laborious. To tackle this, we ask: what can we learn about ccTLDs from public sources? We extract domain names under ccTLDs from 6 years of public data from Certificate Transparency logs and Common Crawl. We compare this against ground truth for 19 ccTLDs for which we have the full DNS zone. We find that public data covers 43%-80% of these ccTLDs, and that coverage grows over time. By also comparing port scan data we then show that these public sources reveal a significant part of the Web presence under a ccTLD. We conclude that in the absence of full access to ccTLDs, domain names learned from public sources can be a good proxy when performing Web censuses.
Aflatoxins are endemic in Kenya. The 2004 outbreak of acute aflatoxicosis in the country was one of the unprecedented epidemics of human aflatoxin poisoning recorded in mycotoxin history. In this study, an elaborate review was performed to synthesize Kenya’s major findings in relation to aflatoxins, their prevalence, detection, quantification, exposure assessment, prevention, and management in various matrices. Data retrieved indicate that the toxins are primarily biosynthesized by Aspergillus flavus and A. parasiticus, with the eastern part of the country reportedly more aflatoxin-prone. Aflatoxins have been reported in maize and maize products (Busaa, chan’gaa, githeri, irio, muthokoi, uji, and ugali), peanuts and its products, rice, cassava, sorghum, millet, yams, beers, dried fish, animal feeds, dairy and herbal products, and sometimes in tandem with other mycotoxins. The highest total aflatoxin concentration of 58,000 μg/kg has been reported in maize. At least 500 acute human illnesses and 200 deaths due to aflatoxins have been reported. The causes and prevalence of aflatoxins have been grossly ascribed to poor agronomic practices, low education levels, and inadequate statutory regulation and sensitization. Low diet diversity has aggravated exposure to aflatoxins in Kenya because maize as a dietetic staple is aflatoxin-prone. Detection and surveillance are only barely adequate, though some exposure assessments have been conducted. There is a need to widen diet diversity as a measure of reducing exposure due to consumption of aflatoxin-contaminated foods.
L. Cegolon, M. Bortolotto, Saverio Bellizzi
et al.
Background. The peak of sexually transmitted infections (STI) among adolescents/young adults suggests a low level of prevention. In order to assess whether the level of sexual health education (SHE), received by several channels, was effective at improving sexual behaviors, we conducted a survey among freshmen from four Italian universities. Methods. This observational cross-sectional study was carried out with an anonymous self-reported paper questionnaire, administered during teaching lectures to university freshmen of the northern (Padua, Bergamo, and Milan campuses) and southern (Palermo campus) parts of the country. Knowledge of STI (a linear numerical score), knowledge of STI prevention (dichotomous variable: yes vs. no) and previous STI occurrence (polytomous variable: “no”; “don’t know”; “yes”) were the outcomes in the statistical analysis. Results. The final number of freshmen surveyed was 4552 (97.9% response rate). The mean age of respondents was 21.4 ± 2.2 years and most of them (70.3%) were females. A total of 60% of students were in a stable romantic relationship. Only 28% respondents knew the most effective methods to prevent STI (i.e., condom and sexual abstinence), with a slightly higher prevalence of correct answers among females (31.3%) than males (25.8%). Students with history of STIs were 5.1%; they reported referring mostly to their general practitioner (GP) (38.1%) rather than discussing the problem with their partner (13.1%). At multivariable analysis, a significantly higher level of STI knowledge was observed in older students (25+ years of age), biomedical students, and those from a non-nuclear family; lower levels were found among students of the University of Palermo, and those who completed a vocational secondary school education. Those who had less knowledge about the most effective tools to prevent STIs included males, students from the University of Palermo, students registered with educational sciences, economics/political sciences, those of foreign nationality, and those whose fathers had lower educational levels. The risk of contracting a STI was significantly lower only in students not in a stable relationship (relative risk ratio, RRR = 0.67; 95% confidence interval, 95%CI = 0.48; 0.94), whereas such risk was significantly higher in students with higher STI knowledge (RRR = 1.15; 95%CI = 1.08; 1.22). Discussion and Conclusions. University freshmen investigated in this study had poor knowledge of STIs and their prevention. Unexpectedly, those with higher levels of knowledge had an increased risk of STIs. There have been no educational interventions—with good quality and long-term follow-ups—that increased the confidence that such SHE programs could have population level effects. A new high-quality study is therefore recommended to assess the effectiveness of an intervention generating behavioral changes; increasing only STI knowledge may not be sufficient.
Ana Valdivia, Cari Hyde-Vaamonde, Julián García-Marcos
This paper discusses an algorithmic tool introduced in the Basque Country (Spain) to assess the risk of intimate partner violence. The algorithm was introduced to address the lack of human experts by automatically calculating the level of violence based on psychometric features such as controlling or violent behaviour. Given that critical literature on risk assessment tools for domestic violence mainly focuses on English-speaking countries, this paper offers an algorithmic accountability analysis in a non-English speaking region. It investigates the algorithmic risks, harms, and limitations associated with the Basque tool. We propose a transdisciplinary approach from a critical statistical and legal perspective. This approach unveils issues and limitations that could lead to unexpected consequences for individuals suffering from partner violence. Moreover, our analysis suggests that the algorithmic tool has a high error rate on severe cases, i.e., cases where the aggressor could murder his partner -- 5 out of 10 high-risk cases are misclassified as low risk -- and that there is a lack of appropriate legal guidelines for judges, the end users of this tool. The paper concludes that this risk assessment tool needs to be urgently evaluated by independent and transdisciplinary experts to better mitigate algorithmic harms in the context of intimate partner violence.
Background: Death certificates are considered the most reliable source of information to compare cause-specific mortality across countries. The aim of the present study was to examine death certificates of persons who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to (a) quantify the number of deaths directly caused by coronavirus 2019 (COVID-19); (b) estimate the most common complications leading to death; and (c) identify the most common comorbidities. Methods: Death certificates of persons who tested positive for SARS-CoV-2 provided to the National Surveillance system were coded according to the 10th edition of the International Classification of Diseases. Deaths due to COVID-19 were defined as those in which COVID-19 was the underlying cause of death. Complications were defined as those conditions reported as originating from COVID-19, and comorbidities were conditions independent of COVID-19. Results: A total of 5311 death certificates of persons dying in March through May 2020 were analysed (16.7% of total deaths). COVID-19 was the underlying cause of death in 88% of cases. Pneumonia and respiratory failure were the most common complications, being identified in 78% and 54% of certificates, respectively. Other complications, including shock, respiratory distress and pulmonary oedema, and heart complications demonstrated a low prevalence, but they were more commonly observed in the 30–59 years age group. Comorbidities were reported in 72% of certificates, with little variation by age and gender. The most common comorbidities were hypertensive heart disease, diabetes, ischaemic heart disease, and neoplasms. Neoplasms and obesity were the main comorbidities among younger people. Discussion: In most persons dying after testing positive for SARS-CoV-2, COVID-19 was the cause directly leading to death. In a large proportion of death certificates, no comorbidities were reported, suggesting that this condition can be fatal in healthy persons. Respiratory complications were common, but non-respiratory complications were also observed.
According to the United Nations, schools' closures have impacted up to 99 per cent of the student population in low and lower-middle-income countries. This research-in-progress report introduces a project on Emergency Remote Teaching (ERT) measures in the ten member states of the Economic Cooperation Organization (ECO) with a focus on the application of Information and Communication Technologies (ICTs) in primary and secondary education levels. The project takes a comparative approach within a resilient ICT-for-Development (ICT4D) framework, where the coping, endurance, and return to pre-crisis functionalities in education systems are studied. The preliminary research demonstrates the impacts of the country's general COVID-19 strategy, the education system in place, and digital infrastructure's level of development on instigating distance-learning platforms. The paper further shows that in addition to access to stable internet connections and digital devices, other infrastructural factors such as access to food, electricity and health services play a significant role in education response planning and implementation. Human factors in the education system, such as teacher training for the usage of ICTs, digital literacy of students and parents, and already existing vulnerabilities in the education system pose challenges to crisis management in the education sector. Other socio-political factors such as attitudes towards girls' education, level of corruption, institutional capacity, and international sanctions or available funds also make the education system less resilient.
We propose how a developing country like Sri Lanka can benefit from privacy-enabled machine learning techniques such as Federated Learning to detect road conditions using crowd-sourced data collection and proposed the idea of implementing a Digital Twin for the national road system in Sri Lanka. Developing countries such as Sri Lanka are far behind in implementing smart road systems and smart cities compared to the developed countries. The proposed work discussed in this paper matches the UN Sustainable Development Goal (SDG) 9: "Build Resilient Infrastructure, Promote Inclusive and Sustainable Industrialization and Foster Innovation". Our proposed work discusses how the government and private sector vehicles that conduct routine trips to collect crowd-sourced data using smartphone devices to identify the road conditions and detect where the potholes, surface unevenness (roughness), and other major distresses are located on the roads. We explore Mobile Edge Computing (MEC) techniques that can bring machine learning intelligence closer to the edge devices where produced data is stored and show how the applications of Federated Learning can be made to detect and improve road conditions. During the second phase of this study, we plan to implement a Digital Twin for the road system in Sri Lanka. We intend to use data provided by both Dedicated and Non-Dedicated systems in the proposed Digital Twin for the road system. As of writing this paper, and best to our knowledge, there is no Digital Twin system implemented for roads and other infrastructure systems in Sri Lanka. The proposed Digital Twin will be one of the first implementations of such systems in Sri Lanka. Lessons learned from this pilot project will benefit other developing countries who wish to follow the same path and make data-driven decisions.
Karandeep Singh, Gabriel Lima, Meeyoung Cha
et al.
The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spreading of potentially harmful misinformation. The infodemic may severely change the pandemic's course by interfering with public health interventions such as wearing masks, social distancing, and vaccination. In particular, the impact of the infodemic on vaccination is critical because it holds the key to reverting to pre-pandemic normalcy. This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic, assesses different populations' susceptibility to false claims, and analyzes its association with vaccine acceptance. Based on responses gathered from over 18,400 individuals from 40 countries, we find a strong association between perceived believability of misinformation and vaccination hesitancy. Additionally, our study shows that only half of the online users exposed to rumors might have seen the fact-checked information. Moreover, depending on the country, between 6% and 37% of individuals considered these rumors believable. Our survey also shows that poorer regions are more susceptible to encountering and believing COVID-19 misinformation. We discuss implications of our findings on public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic. We also highlight fact-checking platforms' role in better identifying and prioritizing claims that are perceived to be believable and have wide exposure. Our findings give insights into better handling of risk communication during the initial phase of a future pandemic.
Stephanie Hirmer, Alycia Leonard, Josephine Tumwesige
et al.
Most well-established data collection methods currently adopted in NLP depend on the assumption of speaker literacy. Consequently, the collected corpora largely fail to represent swathes of the global population, which tend to be some of the most vulnerable and marginalised people in society, and often live in rural developing areas. Such underrepresented groups are thus not only ignored when making modeling and system design decisions, but also prevented from benefiting from development outcomes achieved through data-driven NLP. This paper aims to address the under-representation of illiterate communities in NLP corpora: we identify potential biases and ethical issues that might arise when collecting data from rural communities with high illiteracy rates in Low-Income Countries, and propose a set of practical mitigation strategies to help future work.