Voters in mass elections are notorious for their apparent lack of information about relevant political matters. While some scholars argue that an electorate of well-informed voters is necessary for the production of responsive electoral outcomes, others argue that apparently ignorant voters will suffice because they can adapt their behavior to the complexity of electoral choice. To evaluate the validity of these arguments, I develop and analyze a survey of California voters who faced five complicated insurance reform ballot initiatives. I find that access to a particular class of widely available information shortcuts allowed badly informed voters to emulate the behavior of relatively well informed voters. This finding is suggestive of the conditions under which voters who lack encyclopedic information about the content of electoral debates can nevertheless use information shortcuts to vote as though they were well informed.
Pranggajati Ningrat, Agung Tri Prasetya, Dyah Rini Indrayanti
et al.
Digital science encyclopedias are increasingly used as alternative learning media to support students’ understanding of scientific concepts. However, evidence regarding their research trends and development challenges remains limited. This study aims to analyze research trends, implementation focus, and gaps in the use of digital encyclopedias in science education. A Systematic Literature Review combined with bibliometric analysis was conducted on 36 journal articles published between 2020 and 2025 and indexed in Google Scholar. Keyword co-occurrence and thematic relationships were analyzed using VOSviewer. The results identify three main research clusters: digital encyclopedia development and validation, student-centered implementation, and learning effectiveness with local wisdom integration. Overlay and density visualizations indicate that studies on digital encyclopedias increased between 2022 and 2023 but remain limited compared to other digital learning media. Research involving collaboration with material experts, media experts, and contextual content is still underexplored. These findings suggest that digital encyclopedias positively support science learning but require further development to enhance contextual relevance and sustainability.
The article focuses on the creation of the image of the Muslim community of inner Russia in the historical writings of the pre-revolutionary Tatar-Bashkir elite. The relevance of the study stems from the need to clarify the origin of their present-day national and religious identity. The study materials include established biographical collections, focusing primarily on the biographies of imams and the history of mosques. The authors of the discussed printed works (Sh. Marjani, R. Fahretdin and Muhammad Murad-Ramzi) represent a modernizing society of the middle of the 19th – early 20th centuries, when the framework of the community was made of official institutions. The aim of the research is to trace key ideas representing the worldview as reflected in the studied collections. To this end, the study addresses the following objectives:1) to trace the process of forming the internal image of Russian Muslims; 2) to analyze the interpretation of the conception of ideal spaces through the prism of social constructivism and religious anthropology; 3) to identify specific characteristics of self-representation, writing style and cultural background of the collections. Apart from the historic-genetic, axiological and hermeneutic methods, an anthropological approach to social constructivism theories and ideas of imaginary communities were applied. As a result, it was shown that the collection tends to be centered around the personality of the compiler and his religious and ideological perspective. It is noted that religious ideas and social practices of the community strongly correlate with the development of the unique identity of the Tatar-Muslim community. An incomplete listing of the collection only favors the compilers’ inclination to express their personal likes and dislikes. Unlike modern encyclopedias, the studied collections are somewhat sketchy and do not represent a comprehensive worldview. However, they serve the universal idea of the search for unity by intellectuals and religious leaders of the time. The research fills an important gap in the transformation of the Muslim diaspora in Russia into a secular nation. As a result, the basic principles of the feeling of the collective in the pre-national period were identified. The author concludes that 1) the internal image of Tatar Muslims in Russia was formed both historically and under the influence of Tatar Muslim intellectual elites; 2) social constructivism methodology made it possible to trace the shift from Bulgar as a sacred center to authoritative genealogies of imams which complete the self-representation image of the community; 3) by the beginning of the 20th century, the community center moved from Kazan to the border of Tatars and Bashkirs tribes, the Muftiate in Ufa became a platform for the consolidation of the Turkic-Muslim nationalities of Russia and representation of their religious authority and formal status.
Encyclopedic knowledge platforms are key gateways through which users explore information online. The recent release of Grokipedia, a fully AI-generated encyclopedia, introduces a new alternative to traditional, well-established platforms like Wikipedia. In this context, search engine mechanisms play an important role in guiding users exploratory paths, yet their behavior across different encyclopedic systems remains underexplored. In this work, we address this gap by providing the first comparative analysis of search engine in Wikipedia and Grokipedia. Using nearly 10,000 neutral English words and their substrings as queries, we collect over 70,000 search engine results and examine their semantic alignment, overlap, and topical structure. We find that both platforms frequently generate results that are weakly related to the original query and, in many cases, surface unexpected content starting from innocuous queries. Despite these shared properties, the two systems often produce substantially different recommendation sets for the same query. Through topical annotation and trajectory analysis, we further identify systematic differences in how content categories are surfaced and how search engine results evolve over multiple stages of exploration. Overall, our findings show that unexpected search engine outcomes are a common feature of both the platforms, even though they exhibit discrepancies in terms of topical distribution and query suggestions.
Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski
et al.
The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples together with Protein-Protein Interaction (PPI) networks for cancer classification across 31 different cancer types. The proposed approach combines differential gene expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features. The model architecture is based on the Kolmogorov-Arnold theorem principle and uses trainable univariate functions to enhance interpretability and feature analysis. MOGKAN achieves classification accuracy of 96.28 percent and exhibits low experimental variability in comparison to related deep learning-based models. The biomarkers identified by MOGKAN were validated as cancer-related markers through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates robust predictive performance and interpretability with potential to enhance the translation of complex multi-omics data into clinically actionable cancer diagnostics.
Comparable corpus is a set of topic aligned documents in multiple languages, which are not necessarily translations of each other. These documents are useful for multilingual natural language processing when there is no parallel text available in some domains or languages. In addition, comparable documents are informative because they can tell what is being said about a topic in different languages. In this paper, we present a method to build comparable corpora from Wikipedia encyclopedia and EURONEWS website in English, French and Arabic languages. We further experiment a method to automatically align comparable documents using cross-lingual similarity measures. We investigate two cross-lingual similarity measures to align comparable documents. The first measure is based on bilingual dictionary, and the second measure is based on Latent Semantic Indexing (LSI). Experiments on several corpora show that the Cross-Lingual LSI (CL-LSI) measure outperforms the dictionary based measure. Finally, we collect English and Arabic news documents from the British Broadcast Corporation (BBC) and from ALJAZEERA (JSC) news website respectively. Then we use the CL-LSI similarity measure to automatically align comparable documents of BBC and JSC. The evaluation of the alignment shows that CL-LSI is not only able to align cross-lingual documents at the topic level, but also it is able to do this at the event level.
Elon Musk released Grokipedia on 27 October 2025 to provide an alternative to Wikipedia, the crowdsourced online encyclopedia. In this paper, we provide the first comprehensive analysis of Grokipedia and compare it to a dump of Wikipedia, with a focus on article similarity and citation practices. Although Grokipedia articles are much longer than their corresponding English Wikipedia articles, we find that much of Grokipedia's content (including both articles with and without Creative Commons licenses) is highly derivative of Wikipedia. Nevertheless, citation practices between the sites differ greatly, with Grokipedia citing many more sources deemed "generally unreliable" or "blacklisted" by the English Wikipedia community and low quality by external scholars, including dozens of citations to sites like Stormfront and Infowars. We then analyze article subsets: one about elected officials, one about controversial topics, and one random subset for which we derive article quality and topic. We find that the elected official and controversial article subsets showed less similarity between their Wikipedia version and Grokipedia version than other pages. The random subset illustrates that Grokipedia focused rewriting the highest quality articles on Wikipedia, with a bias towards biographies, politics, society, and history. Finally, we publicly release our nearly-full scrape of Grokipedia, as well as embeddings of the entire Grokipedia corpus.
The launch of Grokipedia, an AI-generated encyclopedia developed by Elon Musk's xAI, was presented as a response to perceived ideological and structural biases in Wikipedia, aiming to produce "truthful" entries using the Grok large language model. Yet whether an AI-driven alternative can escape the biases and limitations of human-edited platforms remains unclear. This study conducts a large-scale computational comparison of 17,790 matched article pairs from the 20,000 most-edited English Wikipedia pages. Using metrics spanning lexical richness, readability, reference density, structural features, and semantic similarity, we assess how closely the two platforms align in form and substance. We find that Grokipedia articles are substantially longer and contain significantly fewer references per word. Moreover, Grokipedia's content divides into two distinct groups: one that remains semantically and stylistically aligned with Wikipedia, and another that diverges sharply. Among the dissimilar articles, we observe a systematic rightward shift in the political bias of frequently cited news media sources, concentrated primarily in entries related to history and religion, and literature and art. More broadly, the findings indicate that AI-generated encyclopedic content departs from established editorial norms, favoring narrative expansion over citation-based verification, raising questions about transparency, provenance, and the governance of knowledge in automated information systems.
Print media such as encyclopedia still holds advantages in learning. As learning media, encyclopedias can be employed to convey current issues occurring in society that are broad and highly important. The high prevalence of toddlers with stunting indicates is crucial to be presented comprehensively. Encyclopedia can act as enrichment by discussing material that can broaden students' perspectives and understanding. However, limited encyclopedias have been developed. This research aims to develop an encyclopedia as a learning medium for Growth and Development material in high school. Enrichment with stunting information is aimed not only at increasing students' understanding, but also at supporting government programs related to efforts to reduce national stunting rates. The research and development method applied uses the ADDIE model which is limited to the first three stages (analysis, design, and development). The initial product is tested for suitability in six aspects, namely presentation, graphics, layout, content, language and use by 5 validators. The validity calculation uses the Aiken's V formula and the final value is 0.93. Referring to the minimum validity value for five validators, the encyclopedia that has been developed meets the valid category in all aspects and indicators. It is hoped that further research can be carried out to implement encyclopedias in learning.
SWEET-Cat (Stars With ExoplanETs Catalogue) was originally introduced in 2013, and since then, the number of confirmed exoplanets has increased significantly. A crucial step for a comprehensive understanding of these new worlds is the precise and homogeneous characterization of their host stars. We used a large number of high-resolution spectra to continue the addition of new stellar parameters for planet-host stars in SWEET-Cat following the new detection of exoplanets listed both at the Extrasolar Planets Encyclopedia and at the NASA exoplanet archive. We obtained high-resolution spectra for a significant number of these planet-host stars, either observed by our team or collected through public archives. For FGK stars, the spectroscopic stellar parameters were derived for the spectra following the same homogeneous process using ARES+MOOG as for the previous SWEET-Cat releases. The stellar properties are combined with the planet properties to study possible correlations that could shed more light into the star-planet connection studies. We increase the number of stars with homogeneous parameters by 232 ($\sim$ 25\% - from 959 to 1191). We then focus on the exoplanets with both mass and radius determined to review the mass-radius relation where we find consistent results with the ones previously reported in the literature. For the massive planets we also revisit the radius anomaly where we confirm a metallicity correlation for the radius anomaly already hinted in previous results.
This research aims to compare the concept of outsourcing in the Law number 13 of 2003 concerning Manpower (Manpower law) compared to the Law Number 6 of 2023 concerning Job Creation (Job Creation Law 2023). This research is important because the Job Creation Law 2023 eliminates job restrictions that are permitted in the outsourcing system. This change is contrary to the workers' desire to eliminate the concept of outsourcing which is considered detrimental to workers. This research uses a type of normative research that relies on secondary data consisting of primary legal materials (law and regulations), secondary legal materials (books and research articles), and tertiary legal materials (encyclopedias and dictionaries). The data was analyzed qualitatively and conclusions were drawn using deductive logic. In general, this research produces new findings in the form of conceptual differences between outsourcing in the Employment Law and the 2023 Job Creation Law. Different concepts will bring different practices accompanied by positive and negative effects. The results of this research show that there is a significant change in the concept of outsourcing in the Job Creation Law 2023, eliminating restrictions so that types of work are more flexible. This opens up opportunities for companies to be able to outsource all sectors of their work, including those that are in their ‘core business’ field. Currently, there is no longer a distinction between ‘work contract agreement’ (perjanjian penyediaan pekerjaan) or ‘worker service provision agreement’ (perjanjian penyediaan jasa pekerja) as regulated in the Manpower Law. There are still options in the work agreement basis for outsourcing, the agreement can be in the form of PKWTT or PKWT. If implemented with PKWT it must be accompanied by a transfer of undertaking protection of employment (TUPE).
Penelitian ini bertujuan untuk melihat perbandingan mengenai konsep alih daya dalam UU Ketenagakerjaan dibandingkan dengan UU Cipta Kerja Tahun 2023. Peneltiian ini penting dilakukan sebab UU Cipta Kerja Tahun 2023 menghilangkan batasan-batasan pekerjaan yang diperbolehkan dalam sistem alih daya. Perubahan ini bertolak belakang dengan keinginan para pekerja selama ini untuk menghilangkan konsep alih daya yang dianggap merugikan pekerja. Penelitian ini menggunakan jenis penelitian normatif yang bertumpu pada data sekunder yang terdiri dari bahan hukum primer yakni peraturan perundang-undangan , bahan hukum sekunder yakni hasil penelitian dan bentuk literasi lainnya, dan bahan hukum tersier yang diperoleh dari ensiklopedia dan kamus hukum. Data kemudian dianalisis secara kualitatif dan kesimpulan ditarik dengan logika deduktif. Penelitian ini secara garis besar menghasilkan temuan baru berupa perbedaan konsep antara alih daya dalam UU Ketenagakerjaan dengan UU Cipta Kerja Tahun 2023. Konsep yang berbeda akan membawa praktik yang berbeda disertai dengan sisi positif dan sisi negatif. Hasil penelitian ini menunjukan bahwa terdapat perubahan konsep yang signifikan mengenai alih daya dalam UU Cipta Kerja Tahun 2023 menghilangkan batasan sehingga lebih fleksibel jenis pekerjaanya. Ini membuka peluang bagi perusahaan untuk dapat mengalih dayakan semua sektor pekerjaanya, termasuk yang menjadi core business-nya. Selain itu saat ini tidak lagi dikenal pemisahan antara “perjanjian pemborongan pekerjaan” atau “perjanjian penyediaan jasa pekerja” seperti yang diatur dalam UU Ketenagakerjaan. Basis perjanjian kerja yang boleh digunakan dalam alih daya masih terdapat opsi dengan PKWTT maupun dengan PKWT. Apabila dilaksanakan dengan PKWT harus disertai dengan pengalihan pelindungan hak pekerja.
The study is dedicated to the issue of information reliability in encyclopedias. The author, who recently published a monograph on the names of Zaporozhian Sich’s settlements [kurins] (this article serves as a reflection on it), illustrates how information from scientific sources, already disproven and considered unfounded, incorrect, and distorting numerous historical facts of Ukraine, often finds its way into the pages of encyclopedias, textbooks, and other literature intended for a broad readership. Using the analysis of specific historical facts related to the Zaporozhian Sich, particularly the names of its settlements, author demonstrates the kind of information that should replace mythical content in reference and educational literature, disseminating more accurate knowledge in society.
The 2023 second edition of a 2006 encyclopedia article on mathematical aspects of quantum field theory in curved spacetimes (QFTCST). Section-titles (with new sections indicated with stars) are: Introduction and preliminaries, Construction of a $*$-algebra for a real linear scalar field on globally hyperbolic spacetimes and some general theorems, *More about (quasifree) Hadamard states, Particle creation and the limitations of the particle concept, Theory of the stress-energy tensor, *More about the intersection of QFTCST with AQFT and the Fewster-Verch No-Go Theorem, Hawking and Unruh effects, *More about (classical and) quantum fields on black hole backgrounds, Non-globally hyperbolic spacetimes and the time-machine question, *More about QFT on non-globally hyperbolic spacetimes, Other related topics and some warnings. The article contains many references. It also includes a review of, and also compares and contrasts, recent results on the implications of QFTCST for the question of the instability of three sorts of Cauchy horizon -- first those inside black holes such as especially Reissner-Nordström-de Sitter and Kerr-de Sitter, second the compactly generated Cauchy horizons of spacetimes in which time-machines get manufactured, and third the Cauchy horizon of the spacetime which is believed to describe evaporating black holes and which underlies (one version of) the black hole information-loss puzzle.
There are some studies that analyze definitions of the term “suicide” in academic works, as well as in generalist dictionaries and encyclopedias in several languages, but there still seems to be no investigation focused on specialized dictionaries and encyclopedias (SDEs) on this topic. Hence the general objective of this study was to describe the suicide SDEs available in the literature. To accomplish this purpose, a basic, quantitative, qualitative, descriptive, bibliographical and documental method was used. The 12 criteria proposed by John Humbley in 2018 for the analysis of SDEs were determined as the main theoretical foundation. From 2017 to 2022, through an integrative review in 6 languages and 4 databases, 8 SDEs were excluded. The 4 included ones were 2 dictionaries and 2 encyclopedias published between 2003 and 2016, 3 in English and 1 in Spanish. One of the most relevant findings was the fact that the SDEs examined try to address the various aspects of this object, instead of adopting a reductionist perspective. However, as only 4 books were analyzed, it was not possible to identify a clear pattern about all 12 criteria and all 4 sources. For example, it is not known which terms would be more feasible to include in a work of this nature nor the optimal way to predicate them. Even so, this paper is a relevant contribution to the studies on language and suicide, providing elements for the evolution of dictionaries and encyclopedias specialized on this binomial.
Revealing relationships between genes and disease phenotypes is a critical problem in biomedical studies. This problem has been challenged by the heterogeneity of diseases. Patients of a perceived same disease may form multiple subgroups, and different subgroups have distinct sets of important genes. It is hence imperative to discover the latent subgroups and reveal the subgroup-specific important genes. Some heterogeneity analysis methods have been proposed in recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate data contamination and ignore the interconnections among genes. Aiming at these shortages, we develop a robust structured heterogeneity analysis approach to identify subgroups, select important genes as well as estimate their effects on the phenotype of interest. Possible data contamination is accommodated by employing the Huber loss function. A sparse overlapping group lasso penalty is imposed to conduct regularization estimation and gene identification, while taking into account the possibly overlapping cluster structure of genes. This approach takes an iterative strategy in the similar spirit of K-means clustering. Simulations demonstrate that the proposed approach outperforms alternatives in revealing the heterogeneity and selecting important genes for each subgroup. The analysis of Cancer Cell Line Encyclopedia data leads to biologically meaningful findings with improved prediction and grouping stability.
Dadaczynski, Kevin, Okan, Orkan, Messer, Melanie
et al.
BackgroundDigital communication technologies are playing an important role in the health communication strategies of governments and public health authorities during the COVID-19 pandemic. The internet and social media have become important sources of health-related information on COVID-19 and on protective behaviors. In addition, the COVID-19 infodemic is spreading faster than the coronavirus itself, which interferes with governmental health-related communication efforts. This jeopardizes national public health containment strategies. Therefore, digital health literacy is a key competence to navigate web-based COVID-19–related information and service environments.
ObjectiveThis study aimed to investigate university students’ digital health literacy and web-based information-seeking behaviors during the early stages of the COVID-19 pandemic in Germany.
MethodsA cross-sectional study among 14,916 university students aged ≥18 years from 130 universities across all 16 federal states of Germany was conducted using a web-based survey. Along with sociodemographic characteristics (sex, age, subjective social status), the measures included five subscales from the Digital Health Literacy Instrument (DHLI), which was adapted to the specific context of the COVID-19 pandemic. Web-based information-seeking behavior was investigated by examining the web-based sources used by university students and the topics that the students searched for in connection with COVID-19. Data were analyzed using univariate and bivariate analyses.
ResultsAcross digital health literacy dimensions, the greatest difficulties could be found for assessing the reliability of health-related information (5964/14,103, 42.3%) and the ability to determine whether the information was written with a commercial interest (5489/14,097, 38.9%). Moreover, the respondents indicated that they most frequently have problems finding the information they are looking for (4282/14,098, 30.4%). When stratified according to sociodemographic characteristics, significant differences were found, with female university students reporting a lower DHLI for the dimensions of “information searching” and “evaluating reliability.” Search engines, news portals, and websites of public bodies were most often used by the respondents as sources to search for information on COVID-19 and related issues. Female students were found to use social media and health portals more frequently, while male students used Wikipedia and other web-based encyclopedias as well as YouTube more often. The use of social media was associated with a low ability to critically evaluate information, while the opposite was observed for the use of public websites.
ConclusionsAlthough digital health literacy is well developed in university students, a significant proportion of students still face difficulties with certain abilities to evaluate information. There is a need to strengthen the digital health literacy capacities of university students using tailored interventions. Improving the quality of health-related information on the internet is also key.
Computer applications to medicine. Medical informatics, Public aspects of medicine