Digital Habitus or Personalization Without Personality

The article reveals the specifics of the use of digital technologies in political campaigns. In particular, based on a comparative analysis, the experience of using these technologies in the political process of the USA, the EU and the Russian Federation is compared. It is noted that artificial intelligence technologies are most often used in political campaigns, namely targeted advertising, Big Data and the use of cryptocurrency based on blockchain technology to finance political campaigns. It is in this direction that the development of regulatory legal acts regulating the use of digital technologies is also underway. Insufficient elaboration of the issues of the use of digital technologies in the political sphere provides significant opportunities for research. The purpose of the article is to identify the main directions of using digital technologies in political campaigns. The following general scientific methods were used to analyze the results obtained: description, analysis, synthesis. An important role was played by the method of comparative analysis, which allows comparing the use of digital technologies in the USA, EU countries and Russia, as well as obtaining the most representative indicators. The authors have developed criteria and indicators for comparing the use of basic digital technologies by different countries and evaluated them on a 5-point scale. The article also used SWOT analysis to identify the strengths and weaknesses of the introduction of digital technologies in political campaigns, as well as to demonstrate the opportunities and threats of their use. The results of comparative and SWOT analysis showed that the use of digital technologies in the Russian Federation and the European Union are approximately at the same level, with the exception of the introduction of Big Data - it is much higher in the EU. The highest degree of implementation of digital technologies is typical for the United States. The article reveals a direct relationship between the degree of regulatory regulation of end-to-end technologies and the level of their dissemination, which indicates the need to maintain a reasonable balance in the legislative regulation of innovative technologies. There is also a tendency to increase the use of digital technologies in Russian political practice, which increases the responsibility of political actors for ensuring the security of personal data of citizens. The article concludes that the development and implementation of digital technologies in modern Russian political life requires the development of a regulatory framework to protect the privacy of citizens and ensure the ethics and transparency of their application. The state should act as a coordinating intermediary between political actors and IT campaigns in order to develop and integrate digital technologies into political campaigns at various levels.

Crisis management has become a critical aspect of modern business and public administration, especially in the face of global crises such as economic recessions, pandemics, and natural disasters. In this context, digital technologies are playing an increasingly important role, providing new tools and approaches for effective crisis management. The definition of crisis management includes a set of measures aimed at identifying, assessing and neutralizing crisis situations, as well as minimizing their negative consequences. It is a management discipline that covers strategic, operational and tactical actions that allow organizations to respond quickly to changes in the external and internal environment. The role of digital technologies in modern management cannot be overestimated. They provide tools for the rapid collection, analysis and processing of information, which is critical in crisis situations. For example, Big Data management systems allow analyzing huge amounts of information in real time, which contributes to a more accurate assessment of the situation and informed decision-making. Cloud technologies provide access to resources and data from anywhere in the world, which is especially important in a crisis when it is necessary to ensure the continuity of business processes and the work of teams on a remote basis. Big data analytics is one of the key components of digital technologies in crisis management. It allows collecting and analyzing data from various sources, including social media, news, internal company systems, etc., to identify potential crises at early stages and predict their development. This enables organizations to respond quickly to threats and minimize negative consequences. Cloud technologies provide flexibility and scalability of the IT infrastructure, allowing organizations to quickly adapt to changes in the external environment and ensure business continuity. They also help reduce IT infrastructure costs and increase resource efficiency. Artificial intelligence and machine learning are powerful tools for automating crisis management processes. They can be used to analyze large amounts of data, detect anomalies, predict the development of crisis situations, and support decision-making. Machine learning algorithms can analyze historical data to identify patterns that precede crises and recommend appropriate actions to prevent them. Digital platforms and tools for communication and collaboration, such as Microsoft Teams, Slack, Zoom, ensure continuous interaction between employees and teams, which is critical in crisis situations. They allow for quick information exchange, virtual meetings, and coordination of actions, which contributes to more effective crisis management. Practical cases of successful use of digital technologies in crisis management include the experience of large corporations, government organizations, and international organizations. For example, Microsoft uses Azure cloud technologies to ensure business continuity during crises such as the COVID-19 pandemic. Government agencies, such as the US Federal Emergency Management Agency (FEMA), use big data management systems and analytics to coordinate actions during natural disasters. Practical cases of successful use of digital technologies in crisis management include the experience of large corporations, government organizations, and international organizations. For example, Microsoft uses Azure cloud technologies to ensure business continuity during crises such as the COVID-19 pandemic. Government agencies, such as the US Federal Emergency Management Agency (FEMA), use big data management systems and analytics to coordinate actions during natural disasters. Recommendations for the implementation of digital technologies in crisis management include strategies and steps for successful implementation, the role of management, IT department and employees, as well as planning and preparation for implementation. It is important that the organization's management understands the importance of digital technologies in crisis management and provides the necessary resources for their implementation. The IT department should be prepared to quickly deploy new technologies and ensure their smooth operation. Employees should be trained to use new tools and technologies, which may require additional training and professional development. Planning and preparation for implementation include the development of a detailed action plan that covers all stages of digital technology implementation, from needs assessment and technology selection to deployment and integration into existing business processes. It is also important to ensure monitoring and evaluation of the effectiveness of the implemented technologies in order to be able to identify and eliminate possible problems in time. The conclusions summarize the importance of digital technologies for crisis management, the main results of the study, and prospects for further research in this area. In particular, it is noted that the use of digital technologies allows organizations to more effectively manage crisis situations, reduce risks and minimize negative consequences. Prospects for further research include the study of new technologies, such as blockchain and the Internet of Things (IoT), and their potential for crisis management.

Owing to recent development in technology, major changes have been noticed in human being's life. Today's lives of human being are becoming more convenient (i.e., in terms of living standard). In current real-world applications, we have shifted our attention from wired devices to wireless devices. As a result, we moved into the era of smart technology, where a lot of Internet devices are connected together in a distributed and decentralized manner. Such Internet-connected devices (ICDs) or Internet of Things (IoTs) engender tremendous data (i.e., via communicating other smart devices). With the tremendous increase in the amount of data, there is a higher requirement to process this huge amount of data (generated through billions of ICDs) using efficient machine learning (ML) algorithms.In the past decade, we refer data mining algorithms to make some decision from collected data-sets. But, due to increasing data on a large scale, data mining fail to handle this data. So, as substitute of data mining algorithms and to refine this information in an efficient manner, we require tradition analytics algorithms, i.e., ML or data mining algorithms. In current scenario, some of the ML algorithms (available to analysis this data) are supervised (used with labeled data), unsupervised (used with unlabelled data) and semi-supervised (work as reward-based learning). Supervised learning algorithms are like linear regression, classification and k-nearest neighbor (KNN), etc. Whereas, unsupervised learning algorithms are clustering, k-means, etc. In general, ML focuses on building the systems that learn and hence improves with the knowledge and experience. Being the heart of artificial intelligence (AI) and data science, ML is gaining popularity day by day. Several algorithms have already been developed (in the past decade) for processing of data, although this field focuses on developing new learning algorithm for big data computability with minimum complexity (i.e., in terms of time and space). ML algorithms are not only applicable to computer science field but also extend to medical, psychological, marketing, manufacturing, automobile, etc.On another side, Big Data including deep learning are the two primary and highly demandable fields of data science. A subset of ML, computer vision or AI, deep learning is used here. The large (or massive) amount of data related to a specific domain which forms Big Data (in form of 5 V's like velocity, volume, value, variety, and veracity) contains valuable information related to various fields like marketing, automobile, finance, cyber security, medical, fraud detection, etc. Such real-world applications are creating a lot of information every day. The valuable (i.e., needful or meaningful) information are required to be processed (or retrieved) from analysis of this unstructured/ large amount of data for further processing of the data for future use (or for prediction). Big organizations have to accord with the tremendous volume of data for prediction, classification, decision making, etc. The use of ML algorithms for big data analytics, which extracts the high-level semantics from the valuable (meaningful) information form the data. It uses hierarchical process for efficient processing and retrieving the complex abstraction from the data.Hence, this chapter discusses several algorithms of ML, to analysis of Big Data. Also, the subset AI like ML algorithms, deep learning algorithms are being discussed here (i.e., to analysis this Big Data for efficient prediction). Later, this chapter focuses on benefits of ML, deep learning algorithms in analyzing tremendous volume of data (i.e., in unsupervised or unstructured form) for numerous complex problems like information retrieval, medical diagnosis, cognitive science, indexing using semantic analysis, data tagging, speech recognition, natural language processing, etc. Also, weakness, raised issues, and challenges (during analysis big data) using (in) ML or deep learning have been discussed in detail. In other words, research gaps in using ML, deep learning algorithms for big data will also be discussed (covering future research aspects/trends). Finally, this chapter discusses the significance of the smart era, computational intelligence, and AI in depth.

The article examines the impact of digital technologies on the procedure for conducting an analysis of an enterprise's activities. It is outlined that digital tools and information and communication technologies provide access to much wider data sets than was previously possible, make it possible to conduct a more rapid and accurate detailed analysis of various indicators of an enterprise's performance, identify trends and dependencies, and predict future events. The importance of conducting a qualitative analysis for making balanced, optimal management decisions and ensuring the effective operation of enterprises is substantiated. The possibilities and advantages of using digital technologies and digital analytical tools at such stages of analysis as collecting and systematizing the necessary information, processing the collected data, conducting quantitative calculations, and presenting the results of the analysis are revealed. A list of digital technologies that should be used at different stages of conducting an analysis at an enterprise is outlined. In particular, such digital technologies as artificial intelligence, machine learning algorithms, Big Data, cloud technologies, blockchain, Power BI, Tableau create better opportunities and higher-quality approaches to collecting large amounts of information, its further storage and processing, calculations, forecasting and presenting analysis results. It is substantiated that by using all the capabilities and advantages of digital analytical tools, cloud technologies and software, it is possible to ensure the receipt of accurate, reliable and high-quality analysis results in a short time, which will really serve as the basis for making effective management decisions. The main risks and challenges caused by the use of the latest digital technologies in the analysis of the enterprise's economic activities are identified, as well as ways to overcome them. It is argued that in order for the use of digital technologies in the analysis to be effective and useful, the enterprise's management needs to take care of ensuring cybersecurity, attracting highly qualified employees and their professional development.

Big data phenotyping in rare diseases: some ethical issues

Scrutinizing environmental governance in a digital age: New ways of seeing, participating, and intervening

We compare the statistical analysis of multidimensional contingency tables by multiple correspondence analysis (MCA) and multiple taxicab correspondence analysis (MTCA). We will show in this paper: First, MTCA and MCA can produce different results. Second, taxicab correspondence analysis of a Burt table is equivalent to centroid correspondence analysis of the indicator matrix. Third, along the first principal axis, the projected response patterns in MTCA will be clustered and the number of cluster points is less than or equal to 1+ the number of variables. Fourth, visual maps produced by MTCA seem to be clearer and more readable in the presence of rarely occurring categories of the variables than the graphical displays produced by MCA. Two well known data sets are analyzed.

Roman notions of social and legal distinction helped to shape the approach of certain pre-Nicene Fathers to the ordering of the church. The social distinction between ordo and plebs and the legal one between honestior and humilior helped these Fathers to differentiate the particular rights and responsibilities of clergy and laity, while the concept of patronage and that of the paterfamilias helped them to define the particular role and authority of the bishop. We see this first articulated in Clement and Hermas of Rome, developed further in Tertullian of Carthage, and then find particular expression in Cyprian of Carthage.

Interorganizational collaboration and the use of new digital technologies, such as artificial intelligence (AI), big data analytics, internet of things (IoT), or blockchain technology, are regarded as key enablers in implementing sustainability and circular economy‐oriented practices. While this is reflected in a few conceptual and case studies, statistical analyses on the topic are rare. No study so far has focused on collaboration, and digital technologies have only been studied in isolation. Therefore, the purpose of this study is to investigate the effect of interorganizational collaboration practices on a firm's circular economy practices and on outcomes (sustainability performance and economic performance), as well as the potentially facilitative role of new digital technologies on both. The research is based on a deductive approach, using a random sample of 112 Austrian manufacturing companies. The study employs partial least squares structural equation modeling (PLS‐SEM), features a multiple‐respondent design, and uses the dynamic capabilities view as a theoretical foundation. The study finds that interorganizational collaboration practices have a strong positive effect on the implementation of sustainability and CE practices, while the use of new digital technologies and general dynamic capabilities do not. The use of digital technologies positively affects only interorganizational collaboration, while general dynamic capabilities serve as an antecedent for both the use of digital technologies and interorganizational collaboration. Regarding the outcomes of CE implementation, the study finds a positive impact on firm‐level sustainability and economic performance. From a theoretical point of view, the study provides a new perspective on the prerequisites for successful CE implementation, highlights the importance of collaboration, and contextualizes the role of new digital technologies and dynamic capabilities. From a practical point of view, based on the positive outcomes found, the study supports arguments in favor of company engagement in CE activity. It also serves to motivate purposive digitization and systems thinking in order to create efficient CE collaboration networks.

CORRESPONDENCE ANALYSIS AND RELATED METHODS IN PRACTICE, Jorg Blasius and Michael Greenacre A simple example Basic method Concepts of correspondence analysis Stacked tables Multiple correspondence analysis Categorical principal components analysis Active and supplementary variables Multiway data Content of the book FROM SIMPLE TO MULTIPLE CORRESPONDENCE ANALYSIS, Michael Greenacre Canonical correlation analysis Geometric approach Supplementary points Discussion and conclusions DIVIDED BY A COMMON LANGUAGE: ANALYZING AND VISUALIZING TWO-WAY ARRAYS, John C. Gower Introduction: two-way tables and data matrices Quantitative variables Categorical variables Fit and scaling Discussion and conclusion NONLINEAR PRINCIPAL COMPONENTS ANALYSIS AND RELATED TECHNIQUES, Jan de Leeuw Linear PCA Least-squares nonlinear PCA Logistic NLPCA Discussion and conclusions Software Notes THE GEOMETRIC ANALYSIS OF STRUCTURED INDIVIDUALS o VARIABLES TABLES, Henry Rouanet PCA and MCA as geometric methods Structured data analysis The basketball study The EPGY study Concluding comments CORRELATIONAL STRUCTURE OF MULTIPLE-CHOICE DATA AS VIEWED FROM DUAL SCALING, Shizuhiko Nishisato Permutations of categories and scaling Principal components analysis and dual scaling Statistics for correlational structure of data Forced classification Correlation between categorical variables Properties of squared item-total correlation Structure of nonlinear correlation Concluding remarks VALIDATION TECHNIQUES IN MULTIPLE CORRESPONDENCE ANALYSIS, Ludovic Lebart External validation Internal validation (resampling techniques) Example of MCA validation Conclusion MULTIPLE CORRESPONDENCE ANALYSIS OF SUBSETS OF RESPONSE CATEGORIES, Michael Greenacre and Rafael Pardo Correspondence analysis of a subset of an indicator matrix Application to women's participation in labor force Subset MCA applied to the Burt matrix Discussion and conclusions SCALING UNIDIMENSIONAL MODELS WITH MULTIPLE CORRESPONDENCE ANALYSIS, Matthijs J. Warrens and Willem J. Heiser The dichotomous Guttman scale The Rasch model The polytomous Guttman scale The graded response model Unimodal models Conclusion THE UNFOLDING FALLACY UNVEILED: VISUALIZING STRUCTURES OF DICHOTOMOUS UNIDIMENSIONAL ITEM-RESPONSE-THEORY DATA BY MULTIPLE CORRESPONDENCE ANALYSIS, Wijbrandt van Schuur and Jorg Blasius Item response models for dominance data Visualizing dominance data Item response models for proximity data Visualizing unfolding data Every two cumulative scales can be represented as a single unfolding scale Consequences for unfolding analysis Discussion REGULARIZED MULTIPLE CORRESPONDENCE ANALYSIS, Yoshio Takane and Heungsun Hwang The method Examples Concluding remarks THE EVALUATION OF DON'T KNOW RESPONSES BY GENERALIZED CANONICAL ANALYSIS, Herbert Matschinger and Matthias C. Angermeyer Method Results Discussion MULTIPLE FACTOR ANALYSIS FOR CONTINGENCY TABLES, Jerome Pages and Monica Becue-Bertaut Tabular conventions Internal correspondence analysis Balancing the influence of the different tables Multiple factor analysis for contingency tables (MFACT) MFACT properties Rules for studying the suitability of MFACT for a data set Conclusion SIMULTANEOUS ANALYSIS: A JOINT STUDY OF SEVERAL CONTINGENCY TABLES WITH DIFFERENT MARGINS, Amaya Zarraga and Beatriz Goitisolo Simultaneous analysis Interpretation rules for simultaneous analysis Comments on the appropriateness of the method Application: study of levels of employment and unemployment according to autonomous community, gender, and training level Conclusions MULTIPLE FACTOR ANALYSIS OF MIXED TABLES OF METRIC AND CATEGORICAL DATA, Elena Abascal, Ignacio Garcia Lautre, and M. Isabel Landaluce Multiple factor analysis MFA of a mixed table: an alternative to PCA and MCA Analysis of voting patterns across provinces in Spain's 2004 general election Conclusions CORRESPONDENCE ANALYSIS AND CLASSIFICATION, Gilbert Saporta and Ndeye Niang Linear methods for classification The Disqual methodology Alternative methods A case study Conclusion MULTIBLOCK CANONICAL CORRELATION ANALYSIS FOR CATEGORICAL VARIABLES: APPLICATION TO EPIDEMIOLOGICAL DATA, Stephanie Bougeard, Mohamed Hanafi, Hicham Nocairi, and El-Mostafa Qannari Multiblock canonical correlation analysis Application Discussion and perspectives PROJECTION-PURSUIT APPROACH FOR CATEGORICAL DATA, Henri Caussinus and Anne Ruiz-Gazen Continuous variables Categorical variables Conclusion CORRESPONDENCE ANALYSIS AND CATEGORICAL CONJOINT MEASUREMENT, Anna Torres-Lacomba Categorical conjoint measurement Correspondence analysis and canonical correlation analysis Correspondence analysis and categorical conjoint analysis Incorporating interactions Discussion and conclusions A THREE-STEP APPROACH TO ASSESSING THE BEHAVIOR OF SURVEY ITEMS IN CROSS-NATIONAL RESEARCH, Jorg Blasius and Victor Thiessen Data Method Solutions Discussion ADDITIVE AND MULTIPLICATIVE MODELS FOR THREE-WAY CONTINGENCY TABLES: DARROCH (1974) REVISITED, Pieter M. Kroonenberg and Carolyn J. Anderson Data and design issues Multiplicative and additive modeling Multiplicative models Additive models: three-way correspondence analysis Categorical principal components analysis Discussion and conclusions A NEW MODEL FOR VISUALIZING INTERACTIONS IN ANALYSIS OF VARIANCE, Patrick J.F. Groenen and Alex J. Koning Holiday-spending data Decomposing interactions Interaction decomposition of holiday spending Conclusions LOGISTIC BIPLOTS. Jose L. Vicente-Villardon, M. Purificacion Galindo-Villardon, and Antonio Blazquez-Zaballos Classical biplots Logistic biplot Application: microarray gene expression data Final remarks References Appendix Index

Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

Following Howarth (2007), this paper examines the relationship between class culture and social distinction in the empirical area of death and dying. For Howarth (2007), the sociological neglect of the end-of-life cultural practices of working-class people carries the danger ‘of privileging middle-class agendas for change, … [t]here is an urgent need for further studies … and a refinement of existing research to draw out social class distinctions’ (p. 433). Drawing on Howarth (2007), and Bourdieu (1984), it is argued below that the connection between death and class reflects and helps to reproduce class-based identities, advantages and conflicts. Whilst class culture can be a supportive resource for working-class people, for example, in ways which reflect solidarity, more typically the classed nature of death brings acute suffering. Relatedly, it is proposed that stereotypes of declining respectability are reflected in some middle-class conceptions of working-class practices and identity connected to death. Such thinking socially positions working-class tastes as objects of disgust, and working-class people as disgusting subjects. It is concluded that, whilst Howarth’s call is much warranted, the debate also needs to problematise the normalisation of middle-class ways to die and grieve.

We present an analysis of a health survey data by multiple cor respondence analysis (MCA) and multiple taxicab correspondence analysis (MTCA), MTCA being a robust L1 variant of MCA. The survey has one passive item, gender, and 22 active substantive items representing health services offered by municipal authorities; each active item has four answer categories: this service is used, never tried, tried with no access, non re sponse. We show that the first principal MTCA factor is perfectly charac terized by the sum score of the category this service is used over all service items. Further, we prove that such a sum score characterization always exists for any survey data.

The current study aimed to identify digital health literacy levels among nurses with respect to their education, role and attitude towards digital technologies. Cross-sectional study. Through convenience sampling, all Registered Nurses, managers/leaders and nurse researchers employed in Hospitals, University Hospitals and Districts were recruited and surveyed using an online questionnaire. The data collection tool assessed: (I) demographics, (II) Digital Health Literacy (DHL) with the Health Literacy Survey19 Digital (HLS19-DIGI) instrument including DHL dealing with digital health information (HL-DIGI), interaction with digital resources for health (HL-DIGI-INT) and use of digital devices for health (HL-DIGI-DD); (III) attitudes on the use of digital technologies in clinical practice. The multiple correspondence analysis was applied to identify three clusters for the education/professional role (A, B, C) and three for digital technologies' use (1, 2, 3). The one-way nonparametric analysis of variance (Kruskal-Wallis test) was applied to compare HL-DIGI, HL-DIGI-INT and the HL-DIGI-DD scores among clusters. Among 551 participants, the median scores of the HL-DIGI, the HL-DIGI-INT and the HL-DIGI-DD questionnaires were 70.2, 72 and 2.00, respectively. The distribution in the clusters 'educational/professional role' was A, (58.8%); B, (16.5%); and C, (24.7%). Nurses in a managerial or coordinator role and with a postgraduate degree used digital resources with greater frequency. The distribution in the clusters 'use of digital technologies' was: 1, (54.6%); 2, (12.2%); and 3, (33.2%). The HL-DIGI-DD and HL-DIGI scores of clusters 1, 2 and 3 differed significantly. DHL among nurses is strongly influenced by the education level, professional role, habits and attitude towards digital technologies. Nurses with coordinator roles used digital technologies with greater frequency and had a higher level of DHL. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were used for reporting. No Patient or Public Contribution. Local Ethical Committee of the Polyclinic of Bari (code: DHL7454, date: 21/09/22).

The generalization of simple (two-variable) correspondence analysis to more than two categorical variables, commonly referred to as multiple correspondence analysis, is neither obvious nor well-defined. We present two alternative ways of generalizing correspondence analysis, one based on the quantification of the variables and intercorrelation relationships, and the other based on the geometric ideas of simple correspondence analysis. We propose a version of multiple correspondence analysis, with adjusted principal inertias, as the method of choice for the geometric definition, since it contains simple correspondence analysis as an exact special case, which is not the situation of the standard generalizations. We also clarify the issue of supplementary point representation and the properties of joint correspondence analysis, a method that visualizes all two-way relationships between the variables. The methodology is illustrated using data on attitudes to science from the International Social Survey Program on Environment in 1993.