Sentences like "She will go to France or Spain, or perhaps to Germany or France." appear formally redundant, yet become acceptable in contexts such as "Mary will go to a philosophy program in France or Spain, or a mathematics program in Germany or France." While this phenomenon has typically been analyzed using symbolic formal representations, we aim to provide a complementary account grounded in artificial neural mechanisms. We first present new behavioral evidence from humans and large language models demonstrating the robustness of this apparent non-redundancy across contexts. We then show that, in language models, redundancy avoidance arises from two interacting mechanisms: models learn to bind contextually relevant information to repeated lexical items, and Transformer induction heads selectively attend to these context-licensed representations. We argue that this neural explanation sheds light on the mechanisms underlying context-sensitive semantic interpretation, and that it complements existing symbolic analyses.
Since the Google Spain judgment of the Court of Justice of the European Union, Europeans have, under certain conditions, the right to have search results for their name delisted. This paper examines how the Google Spain judgment has been applied in the Netherlands. Since the Google Spain judgment, Dutch courts have decided on two cases regarding delisting requests. In both cases, the Dutch courts considered freedom of expression aspects of delisting more thoroughly than the Court of Justice. However, the effect of the Google Spain judgment on freedom of expression is difficult to assess, as search engine operators decide about most delisting requests without disclosing much about their decisions.
This paper presents a longitudinal open dataset of Spanish public procurement extracted from the Official State Gazette (BOE) covering the period 2014-2024. The dataset integrates structured information on contracts, contracting authorities, suppliers, amounts, and procedures, enabling large-scale quantitative analysis of public procurement dynamics in Spain. We describe the data extraction and normalization pipeline, provide descriptive statistical analyses of temporal and sectoral trends, and discuss potential applications in transparency research, public policy evaluation, and computational social science. The dataset is released to facilitate reproducible research on public procurement and government contracting.
Growing concerns about housing affordability have prompted the adoption of rent control policies and renewed debates over their effectiveness. This paper provides the first empirical evaluation of the 2024 rent control policy implemented in Catalonia under Spain's new national housing law. To identify the causal effect of the policy on the rental market, I use municipality-level administrative data and implement several difference-in-differences strategies and event study designs. The results point to a reduction in tenancy agreements and a less robust decrease in rental price growth. While the findings highlight important short-term consequences of rent control, they also underscore the need for caution due to data limitations and limited robustness in some estimates.
Wildfires pose a threat to ecosystems, economies and public safety, particularly in Mediterranean regions such as Spain. Accurate predictive models require high-resolution spatio-temporal data to capture complex dynamics of environmental and human factors. To address the scarcity of fine-grained wildfire datasets in Spain, we introduce IberFire: a spatio-temporal dataset with 1 km x 1 km x 1-day resolution, covering mainland Spain and the Balearic Islands from December 2007 to December 2024. IberFire integrates 120 features across eight categories: auxiliary data, fire history, geography, topography, meteorology, vegetation indices, human activity and land cover. All features and processing rely on open-access data and tools, with a publicly available codebase ensuring transparency and applicability. IberFire offers enhanced spatial granularity and feature diversity compared to existing European datasets, and provides a reproducible framework. It supports advanced wildfire risk modelling via Machine Learning and Deep Learning, facilitates climate trend analysis, and informs fire prevention and land management strategies. The dataset is freely available on Zenodo to promote open res
High-resolution energy consumption and emissions datasets are essential for localized policy-making, resource optimization, and climate action planning. They enable municipalities to monitor mitigation strategies and foster engagement among governments, businesses, and communities. However, smaller municipalities often face data limitations that hinder tailored climate strategies. This study generates detailed final energy consumption and emissions data at the local administrative level for Germany and Spain. Using national datasets, we apply spatial disaggregation techniques with open data sources. A key innovation is the application of XGBoost for imputing missing data, combined with a stepwise spatial disaggregation process incorporating district- and province-level statistics. Prioritizing reproducibility, our open-data approach provides a scalable framework for municipalities to develop actionable climate plans. To ensure transparency, we assess the reliability of imputed values and assign confidence ratings to the disaggregated data.
AI is transforming medical practice and redefining the competencies that future healthcare professionals need to master. Despite international recommendations, the integration of AI into Medicine curricula in Spain had not been systematically evaluated until now. A cross-sectional study (July-September 2025) including Spanish universities offering the official degree in Medicine, according to the 'Register of Universities, Centers and Degrees (Registro de Universidades, Centros y Títulos RUCT)'. Curricula and publicly available institutional documentation were reviewed to identify courses and competencies related to AI in the 2025-2026 academic year. The analysis was performed using descriptive statistics. Of the 52 universities analyzed, ten (19.2%) offer specific AI courses, whereas 36 (69.2%) include no related content. Most of the identified courses are elective, with a credit load ranging from three to six ECTS, representing on average 1.17% of the total 360 credits of the degree. The University of Jaén is the only institution offering a compulsory course with AI content. The territorial analysis reveals marked disparities: Andalusia leads with 55.5% of its universities incorp
In this paper, we present the main actions of the Women in Physics Group of the Spanish Royal Physics Society over the period of 2022 to 2023, in which we celebrated the 20th anniversary of the group. We also outline relevant equality initiatives implemented during this period by the Spanish Government as well as analyse their impact on the status of women in Physics in our country. In 2023, our scientific society approved the Gender Equality Plan, thus becoming a pioneer scientific society in Spain in implementing this relevant measure
We study the evolution of intergenerational educational mobility and related distributional statistics in Spain. Over recent decades, mobility has risen by one-third, coinciding with pronounced declines in inequality and assortative mating among the same cohorts. To explore these patterns, we examine regional correlates of mobility, using split-sample techniques. A key finding from both national and regional analyses is the close association between mobility and assortative mating: spousal sorting accounts for nearly half of the regional variation in intergenerational correlations and also appears to be a key mediator of the negative relationship between inequality and mobility documented in recent studies.
The exceptional reception of Pietro Metastasio's works during the eighteenth century, all over Europe and in the Iberian Peninsula in particular, is well documented. Due to that unparalleled success, it is possible to ascertain Spain and Portugal's participation in international, contemporary tastes and artistic webs, applicable to both composers and performers. However, this internationalisation needs to be nuanced, as some characteristics of the repertoire specifically written for the Peninsula indicate that their court audiences may have had expectations, both social and strictly musical, different from those of the public in opera theatres elsewhere in the continent. In this light, this article investigates in what ways the style of five composers in the international scene - Perez, Galuppi, Jommelli, Conforto, and Corselli - varied when commissioned to write opera seria for the Iberian courts. The statistical analysis of fifteen settings especially written for the court theatres in Madrid and Lisbon, in comparison to the average data extracted from a corpus of 2,404 arias from 126 versions of a select number of Metastasian librettos, allows us to evaluate some particular usage
When reviewing a job application letter, going on a first date, or considering doing business with someone, the first thing many people do is entering the person's name in a search engine. A search engine can point searchers to information that would otherwise have remained obscure. If somebody searched for the name of Spanish lawyer Mario Costeja González, Google showed search results that included a link to a 1998 newspaper announcement implying he had financial troubles at the time. González wanted Google to stop showing those links and started a procedure in Spain. After some legal wrangling, the Spanish Audiencia Nacional (National High Court) asked the Court of Justice of the European Union (CJEU) for advice on the application of the Data Protection Directive, which led to the controversial judgment in Google Spain. In its judgment, the CJEU holds that people, under certain conditions, have the right to have search results for their name delisted. This right can also extend to lawfully published information.
This study analyzes the financial resilience of agricultural and food production companies in Spain amid the Ukraine-Russia war using cluster analysis based on financial ratios. This research utilizes centered log-ratios to transform financial ratios for compositional data analysis. The dataset comprises financial information from 1197 firms in Spain's agricultural and food sectors over the period 2021-2023. The analysis reveals distinct clusters of firms with varying financial performance, characterized by metrics of solvency and profitability. The results highlight an increase in resilient firms by 2023, underscoring sectoral adaptation to the conflict's economic challenges. These findings together provide insights for stakeholders and policymakers to improve sectorial stability and strategic planning.
Spain is one of the eight EU-27 countries that failed to reduce early school leaving (ESL) below 10% in 2020, and now faces the challenge of achieving a rate below 9% by 2030. The determinants of this phenomenon are usually studied using cross-sectional data at the micro-level and without differentiation by gender. In this study, we analyse it for the first time for Spain using panel data (between 2002-2020), taking into account the high regional inequalities at the macroeconomic level and the masculinisation of the phenomenon. The results show a positive relationship between ESL and socioeconomic variables such as the adolescent fertility rate, immigration, unemployment or the weight of the industrial and construction sectors in the regional economy, with significant gender differences that invite us to discuss educational policies. Surprisingly, youth unemployment has only small but significant impact on female ESL.
We are 600 million Spanish speakers. We launched the #Somos600M Project because the diversity of the languages from LATAM, the Caribbean and Spain needs to be represented in Artificial Intelligence (AI) systems. Despite being the 7.5% of the world population, there is no open dataset to instruction-tune large language models (LLMs), nor a leaderboard to evaluate and compare them. In this paper, we present how we have created as an international open-source community the first versions of the instruction and evaluation datasets, indispensable resources for the advancement of Natural Language Processing (NLP) in our languages.
This short note presents preliminary findings on the impact of the October 2024 floods on cultural heritage sites in Valencia, Spain. Using publicly available data, we assess the extent of potential damage by overlaying flood maps with heritage site coordinates. We identify that 3.3% of heritage sites in the region have been potentially impacted, with churches and shrines (81), outdoor religious iconography (78), and historic irrigation features (45) being the most heavily affected. Our analysis utilizes data from OpenStreetMap and listings from the Generalitat Valenciana, suggesting that while OpenStreetMap's crowd-sourced data can provide useful estimates of the proportion of impacted sites, it may not be suitable for a detailed damage assessment. By sharing this data openly, we aim to contribute to international efforts in preserving cultural heritage after the disaster and provide a foundation for future assessments of heritage site vulnerability to climate-related events.
This paper investigates citizens' counter-strategies to the use of Artificial Intelligence (AI) by law enforcement agencies (LEAs). Based on information from three countries (Greece, Italy and Spain) we demonstrate disparities in the likelihood of ten specific counter-strategies. We further identified factors that increase the propensity for counter-strategies. Our study provides an important new perspective to societal impacts of security-focused AI applications by illustrating the conscious, strategic choices by citizens when confronted with AI capabilities for LEAs.
Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on observational data, so it remains an open question whether they can plausibly extrapolate unseen conditions and changes in future emissions scenarios. Here we focus on this problem as the main drawback for the operationalization of these methods and present the results of an intercomparison experiment to evaluate the performance and extrapolation capability of existing models using a common experimental framework, taking into account the sensitivity of results to different training replicas. We focus on minimum and maximum temperatures and precipitation over Spain, a region with a range of climatic conditions with different influential regional processes. We conclude with a discussion of the findings, limitations of existing methods, and prospects for future development.
After decades of improvements in the employment conditions of females in Spain, this process came to a sudden stop with the Great Spanish Recession of 2008. In this contribution, we analyse a large longitudinal corpus of national and regional news outlets employing advanced Natural Language Processing techniques to capture the valence of mentions of gender inequality expressed in the Spanish press. The automatic analysis of the news articles does indeed capture the known hardships faced by females in the Spanish labour market. Our approach can be straightforwardly generalised to other topics of interest. Assessing the sentiment and moral values expressed in the articles, we notice that females are, in the majority of cases, concerned more than males when there is a deterioration in the overall labour market conditions, based on newspaper articles. This behaviour has been present in the entire period of study (2000--2022) and looked particularly pronounced during the economic crisis of 2008 and the recent COVID-19 pandemic. Most of the time, this phenomenon looks to be more pronounced at the regional level, perhaps caused by a significant focus on local labour markets rather than on
We present an extensive archaeoastronomical study of the orientations of seventeenth- and eighteenth-century Jesuit churches in the lands of the historic viceroyalty of New Spain. Our sample includes forty-one chapels and churches located mainly in present-day Mexico, which documentary sources indicate were built by the Society, and for which we measured the azimuths and heights of the horizon of their principal axes using satellite imagery and digital elevation models. Our results show that neither the orientation diagram nor the statistical analysis derived from the sample declination histogram can select a particular orientation pattern with an adequate level of confidence. We suggest some possible explanations for our results, discussing these North American churches within a broader cultural and geographical context that includes previous studies involving Jesuit mission churches in South America. Based on the analysis of the data presented here, we conclude that the orientation of Jesuit churches in the viceroyalty of New Spain most likely does not follow a well-defined prescription.
Abundant evidence has tracked the labour market and health assimilation of immigrants, including static analyses of differences in how foreign-born and native-born residents consume health care services. However, we know much less about how migrants' patterns of health care usage evolve with time of residence, especially in countries providing universal or quasi-universal coverage. We investigate this process in Spain by combining all the available waves of the local health survey, which allows us to separately identify period, cohort, and assimilation effects. We find that the evidence of health assimilation is limited and solely applies to migrant females' visits to general practitioners. Nevertheless, the differential effects of ageing on health care use between foreign-born and native-born populations contributes to the convergence of utilisation patterns in most health services after 20 years in Spain. Substantial heterogeneity over time and by region of origin both suggest that studies modelling future welfare state finances would benefit from a more thorough assessment of migration.