Language Versions Of Wikipedia Research Articles

The Most Cited Scientific Information Sources in Wikipedia Articles Across Various Languages

Ensuring the accuracy of information in Wikipedia articles requires the use of reliable sources that can be assessed by encyclopedia readers. However, the determination of source reliability can be subjective, with variations based on the language and subject matter of the article. Consequently, each language version of Wikipedia may have its own guidelines for judging the trustworthiness of sources. Some Wikipedia citations lead to scientific resources, which are usually deemed more reliable than websites due to their rigorous peer-review procedures and publication by esteemed academic publishers. This implies that the data presented in these scientific sources has been meticulously examined by specialists in the relevant area, providing a higher level of precision and trustworthiness. In this study, 332,424,439 references from 61,218,277 Wikipedia articles across 309 language versions were analyzed to identify citations to scientific publications. Additionally, OpenAlex was used to find unified metadata of important sources of information of multilingual Wikipedia.

Quantifying Americanization: Coverage of American Topics in Different Wikipedias

As one of the most popular sources of information in the world, Wikipedia is edited by a large, global community of contributors. User-generated nature of this online encyclopedia ensures that the information reflects a wide range of topics. Hovewer, Wikipedia articles are created and edited independently in each language version. Therefore, some topics may be presented with varying degrees of completeness depending on their importance in a particular language community. In this paper, we quantified the concept of Americanization on a global scale through comparative analysis of the coverage of American topics in different language versions of Wikipedia. For this purpose, we analyzed over 90 million Wikidata items and 40 million Wikipedia articles in 58 languages. We discussed whether Americanization is more or less dominant in different languages, regions, and cultures. We showed that the interest in American topics is not universal. Western, developed countries are more Americanized (more interested in topics related to America) than the rest of the world. This is the first global, quantitative confirmation of issues often hypothesized, or assumed, in the literature on Americanization and related phenomena. This study shows that Wikipedia and Wikidata can allow quantification of social science concepts that previously were considered not realistically measurable. Finally, the presented research is also relevant to the discourses on the biases of Wikipedia.

Public interests in mental health topics in COVID-19: evidence from Wikipedia searches

ABSTRACT Objective The aim of the study was to explore community interest in mental health topics during and before the COVID-19 pandemic. Method We gathered and analysed a large dataset (over 3 billion) of views of 1763 English Wikipedia mental health articles, and their counterparts in nine other language versions of Wikipedia, between the period of January 1, 2016, and December 31, 2020. Results The results of this study show that the patterns of Wikipedia searches during the pandemic changed. Interest in articles about insecurities and paraphilias increased. There were more searches for child abuse-related topics. Views for depression decreased. Discussion We hypothesise that during the lockdown and enforced video communication, people’s concern about self-image and privacy arose, and the tendency to follow one’s desires online increased. There may be a possible interaction between media coverage and the novelty with the saturation of a given concept in the public discourse. The observations on the online interests of peers may become a sensitive predictor and early sign of arising new phenomena and increase of ongoing public health problems. The results of this study and future in-depth research may contribute to providing preventive programs including screening digital tools, and online apps for early intervention.

GLAM Wikiprojects as a Form of Organization of Cooperation Between Wikipedia and Cultural Institutions

The development of new communication technologies enabled the digitalization of the knowledge resources of cultural institutions all over the world and made them available to a wider audience. This was possible through the cooperation of cultural institutions with Wikipedia’s WikiProjects and engagement of the users from numerous countries. It seems important to recognize if this form of cooperation between Wikipedians and institutions is effective and attracts enough participants. The article takes up the subject of the character and effects of this cooperation. The aim of the paper is to study the activity of Wikipedians, including newcomers, in GLAM (Galleries, Libraries, Archives, and Museums) WikiProjects in several language versions of Wikipedia as an open knowledge project. The research questions concerned the characteristics of the activity of Wikipedians in GLAM WikiProjects and the differences between the behaviour of Wikipedians in GLAM WikiProjects in different language versions. The method used in the research is the content analysis, which includes quantitative tools. The author acquired and calculated the data on the activity of GLAM Wikiprojects participants thanks to the Wikipedia xTools tool. The calculations were made on participants of GLAM WikiProjects from five language versions of Wikipedia. The main conclusion from the research is that the results of this kind of organization of work, especially in terms of newcomers, are not satisfactory. The most effective participants are the coordinators of the projects or/and the Wikipedians with the highest seniority. Newly acquired participants most often do not continue their activity for long time. However, there are some differences in different language versions of Wikipedia. English one has better results in retaining and engaging new users. What is interesting is that despite the wide popularization and engagement of the GLAM institutions in the collaboration with Wikipedia, there is a relatively low interest of new Wikipedians in GLAM Wikiprojects. In order to reach a successful cooperation, it is significant to improve the effectiveness of welcoming, engaging, and retaining newcomers in the projects. The results may be of use to the practitioners of the virtual communities of practice which focus on creating and sharing knowledge.

Meaning of the Power Users in the Wikipedia Working Environment

One of the most popular sites to observe collaborative content creation on the Internet is Wikipedia. A significant part of the publication devoted to this encyclopaedia focuses on special forms of collaboration, such as WikiProjects, which allow to effective work sharing. The aim of the paper was to identify power users working in public spaces of Wikipedia, especially in WikiProjects, and to determine their importance for this form of cooperation in knowledge sharing. The research question was: what is the meaning of the Power Users in Wikipedia? The methods used in the paper were content analysis of activity logs of Wikipedians and participatory observation. The author presents the results of his own research on the total number of WikiProject users in the two language versions of Wikipedia: Polish and Korean. The research presented the existence of power users in the Polish and Korean versions. There was a moderately strong relationship between the length of time one remained active on Wikipedia and the time of joining as a Wikipedian, and a weak relationship between seniority and the number of WikiProjects in which the user was active. In turn, participatory observation identified the roles of power users, mainly as initiators of action in Wikipedia. The presented research results could be used mostly for coordinators of online communities, but also for the initiators of the new undertakings in cyberspace.

Forms of Adaptation of Newcomers in Wikipedia as Online Sharing Knowledge Project

Abstract: Wikipedia is one of the largest virtual communities of practice aimed at building and sharing knowledge online. Its ambitious goals require the cooperation of millions of users around the world. The development of the project has slowed down over the last few years, hence the acquisition and retention of new users is becoming more and more challenging. Consequently, properly organized adaptation, and encouragement of newcomers to become a part of the community emerges as a crucial issue for the existence of Wikipedia.
 This paper presents the results of the quantitative research carried out with the use of the content analysis method on the different language versions of Wikipedia. The issues investigated included the following aspects: the level of advancement of the newcomers’ adaptation process, the use of various adaptation tools, forms of welcoming and enhancing newcomers to learn and edit the Wikipedia content, while also their potential effect on the productivity and engagement of users.
 The results of the research indicate great diversity among the tools used, depending on the language version of the project. Not all the statements directed to the newcomers are encouraging and friendly, as some cultures create more restrictive and punitive content than the others. The latter leaves the newcomer with the responsibility for the process of learning the rules of behaviour of the Wikipedia community. The more advanced the adaptation process is, the more active and productive users are. The conclusions of the research may be of use to different virtual communities of practice and organizations which face the problem of adapting newcomers in the online activity connected with knowledge sharing.

Identification of Important Web Sources of Information on Wikipedia across various Topics and Languages

Despite the fact, that over 21 years Wikipedia is edited by volunteers from all over the world with different views, culture, education and competences, this free encyclopedia is one of the most popular source of knowledge in the Internet. Freedom to edit does not mean, that you can put there whatever content you want. One of the core rules of Wikipedia says, that information in its articles should be based on reliable sources and Wikipedia readers must be able to verify particular facts in text. However, reliability is a subjective concept and a reputation of the same source can be assessed diffidently depending on a person (or group of persons), language and topic. So each language version of Wikipedia may have own rules or criteria on how the website must be assessed before it can be used as a source in references. At the same time, nowadays there are over 1 billion websites on the Internet and only few developed language chapters of the encyclopedia contains non-exhaustive lists of less than 1 thousand popular websites with reliability assessment. Additionally, since reputation of the source can be changed during the time, such lists must be updated regularly.This study presents result of important web sources identification based on analysis of over 230 million references that were extracted from over 40 million Wikipedia article of 42 most developed language version. Additionally, general statistics on references usage for each considered Wikipedia language were counted, including average number of references, number of unique references, scientific score, number of websites in references. Next, Wikipedia articles were assigned to different topics in each considered language. This allows to find differences in reliability and popularity of the same sources of information between Wikipedia languages, as well as find important websites in specific areas of knowledge.

Uneven geographies in the various language editions of Wikipedia: the case of Ukrainian cities

The paper tackles the issue of uneven geographical representations on Wikipedia, the most visible and powerful user-generated encyclopaedia. In particular, it addresses language imbalances on Wikipedia with regard to geographical information and uneven spatial patterns of territory coverage on the different language versions in an attempt to verify expectations about the cultural factors that influence these imbalances and uneven spatial patterns. Ukraine is a promising case for testing the formulated expectations, as it has a large number of neighbouring countries, and most of them had political and cultural influence on its territory in the past. The volumes (word counts) of articles about the Ukrainian cities were analysed for seven language versions of Wikipedia, including the Ukrainian version and the versions of all bordering countries. The results show that historical geography is the strongest and central factor, and most of the key relic borders (former boundaries) can be traced. Ethnic composition appears to be another important factor, although weaker than the previous one. The role of the border factor is often unclear, but in some cases it definitely makes an impact and therefore cannot be completely ignored. Thus, the geographies of Wikipedia are not indifferent to the issues of ethnicity and geopolitics. The research calls into question the ability of modern Wikipedia to be a reliable and balanced source of geographical knowledge, as the described imbalances may create lopsided and biased geographical representations in people from different countries and nations.

Assessing Public Interest Based on Wikipedia's Most Visited Medical Articles During the SARS-CoV-2 Outbreak: Search Trends Analysis.

BackgroundIn the current era of widespread access to the internet, we can monitor public interest in a topic via information-targeted web browsing. We sought to provide direct proof of the global population’s altered use of Wikipedia medical knowledge resulting from the new COVID-19 pandemic and related global restrictions.ObjectiveWe aimed to identify temporal search trends and quantify changes in access to Wikipedia Medicine Project articles that were related to the COVID-19 pandemic.MethodsWe performed a retrospective analysis of medical articles across nine language versions of Wikipedia and country-specific statistics for registered COVID-19 deaths. The observed patterns were compared to a forecast model of Wikipedia use, which was trained on data from 2015 to 2019. The model comprehensively analyzed specific articles and similarities between access count data from before (ie, several years prior) and during the COVID-19 pandemic. Wikipedia articles that were linked to those directly associated with the pandemic were evaluated in terms of degrees of separation and analyzed to identify similarities in access counts. We assessed the correlation between article access counts and the number of diagnosed COVID-19 cases and deaths to identify factors that drove interest in these articles and shifts in public interest during the subsequent phases of the pandemic.ResultsWe observed a significant (P<.001) increase in the number of entries on Wikipedia medical articles during the pandemic period. The increased interest in COVID-19–related articles temporally correlated with the number of global COVID-19 deaths and consistently correlated with the number of region-specific COVID-19 deaths. Articles with low degrees of separation were significantly similar (P<.001) in terms of access patterns that were indicative of information-seeking patterns.ConclusionsThe analysis of Wikipedia medical article popularity could be a viable method for epidemiologic surveillance, as it provides important information about the reasons behind public attention and factors that sustain public interest in the long term. Moreover, Wikipedia users can potentially be directed to credible and valuable information sources that are linked with the most prominent articles.

Cryptocurrencies Perception Using Wikipedia and Google Trends

In this research we presented different approaches to investigate the possible relationships between the largest crowd-based knowledge source and the market potential of particular cryptocurrencies. Identification of such relations is crucial because their existence may be used to create a broad spectrum of analyses and reports about cryptocurrency projects and to obtain a comprehensive outlook of the blockchain domain. The activities on the blockchain reach different levels of anonymity which renders them hard objects of studies. In particular, the standard tools used to characterize social trends and variables that describe cryptocurrencies’ situations are unsuitable to be used in the environment that extensively employs cryptographic techniques to hide real users. The employment of Wikipedia to trace crypto assets value need examination because the portal allows gathering of different opinions—content of the articles is edited by a group of people. Consequently, the information can be more attractive and useful for the readers than in case of non-collaborative sources of information. Wikipedia Articles often appears in the premium position of such search engines as Google, Bing, Yahoo and others. One may expect different demand on information about particular cryptocurrency depending on the different events (e.g., sharp fluctuations of price). Wikipedia offers only information about cryptocurrencies that are important from the point of view of language community of the users in Wikipedia. This “filter” helps to better identify those cryptocurrencies that have a significant influence on the regional markets. The models encompass linkages between different variables and properties. In one model cryptocurrency projects are ranked with the means of articles sentiment and quality. In another model, Wikipedia visits are linked to cryptocurrencies’ popularity. Additionally, the interactions between information demand in different Wikipedia language versions are elaborated. They are used to assess the geographical esteem of certain crypto coins. The information about the legal status of cryptocurrency technologies in different states that are offered by Wikipedia is used in another proposed model. It allows assessment of the adoption of cryptocurrencies in a given legislature. Finally, a model is developed that joins Wikipedia articles editions and deletions with the social sentiment towards particular cryptocurrency projects. The mentioned analytical purposes that permit assessment of the popularity of blockchain technologies in different local communities are not the only results of the paper. The models can show which country has the biggest demand on particular cryptocurrencies, such as Bitcoin, Ethereum, Ripple, Bitcoin Cash, Monero, Litecoin, Dogecoin and others.

Collectively biased representations of the past: Ingroup Bias in Wikipedia articles about intergroup conflicts.

Individuals tend to present their own group (the ingroup) in a systematically more favourable way (ingroup bias). By examining socially negotiated and publicly accessible Wikipedia articles about intergroup conflicts, we investigated ingroup bias at a collective level. Specifically, we compared articles about the same intergroup conflicts (e.g., the Falklands War) in the corresponding language versions of Wikipedia (e.g., the Spanish and English Wikipedia articles about the Falklands War). Study 1 featured a content coding of translated Wikipedia articles by trained raters, which showed that articles systematically presented the ingroup in a more favourable way (e.g., Argentina in the Spanish article and the United Kingdom in the English article) and, in reverse, the outgroup as more immoral and more responsible for the conflict. These findings were replicated and extended in Study 2, which was limited to the lead sections of articles but included considerably more conflicts and many participants instead of a few trained coders. This procedure allowed for separate analyses for each conflict, which showed considerable variance in the results pattern with a stronger ingroup bias for (1) more recent conflicts and (2) conflicts in which the proportion of ingroup members among the top editors was larger. Finally, a third study ruled out that these effects were driven by translations or the raters' own nationality. Therefore, this paper is the first to demonstrate ingroup bias in Wikipedia - a finding that is of practical as well as theoretical relevance as we outline in the discussion.

Multilingual Ranking of Wikipedia Articles with Quality and Popularity Assessment in Different Topics

On Wikipedia, articles about various topics can be created and edited independently in each language version. Therefore, the quality of information about the same topic depends on the language. Any interested user can improve an article and that improvement may depend on the popularity of the article. The goal of this study is to show what topics are best represented in different language versions of Wikipedia using results of quality assessment for over 39 million articles in 55 languages. In this paper, we also analyze how popular selected topics are among readers and authors in various languages. We used two approaches to assign articles to various topics. First, we selected 27 main multilingual categories and analyzed all their connections with sub-categories based on information extracted from over 10 million categories in 55 language versions. To classify the articles to one of the 27 main categories, we took into account over 400 million links from articles to over 10 million categories and over 26 million links between categories. In the second approach, we used data from DBpedia and Wikidata. We also showed how the results of the study can be used to build local and global rankings of the Wikipedia content.

Using Crowd-curation to Improve Taxon Annotations on the Wikimedia Infrastructure

Here we present how two independent infrastructures, Wikimedia and iNaturalist, can be jointly leveraged to improve content on both platforms. iNaturalist.org began as a Master's final project in 2008 and grew to a globally used app to help identify biodiversity. The community behind iNaturalist consists of citizen scientists, who record a species existence through photos or sound recordings. The Wikimedia Foundation provides a spectrum of resources, of which Wikipedia is the most famous sibling. Other siblings we address here are Wikimedia Commons and Wikidata. Commons is the platform where open licensed media can be shared. Wikidata is the linked knowledge graph, where public data can be stored as structured data. Basically, data goes to Wikidata, images and recordings go to Commons and text goes to Wikipedia. Initially, both Commons and Wikidata served primarily the approximately 300 language versions of Wikipedia. However, nowadays both Commons and Wikidata are also being used as is, that is in other contexts than Wikipedia. Although iNaturalist and the Wikimedia family of repositories thrive as independent infrastructures and are thus maintained independently from each other, they can be mutually beneficial to each other. Content created by the iNaturalist community can be very valuable to the Wikimedia community. Firstly, observations in the form of photos and sound recordings can be stored on iNaturalist, with an open and compatible license that can provide valuable illustrations and structured knowledge on biodiversity. Wikipedia articles can be enriched with already approximately 1.2M photos from observations. Furthermore, the assertions made by the iNaturalist community can act as references in various Wikidata claims or Wikipedia articles. In many cases in Wikipedia, we have to rely on personal annotations by the picture taker, who stores it on Wikimedia’s multimedia Commons. iNaturalist provides images of organisms with stronger - peer-reviewed - assertions on the subject in the picture. When a cat is called a cat in Wikipedia or Commons, it is the iNaturalist community that either approves or rejects that claim. In the opposite direction, iNaturalist relies on knowledge described in Wikipedia. It includes Wikipedia articles about taxa on its website. Images can also be uploaded into iNaturalist from Commons. It is therefore possible to add images of museum specimens into iNaturalist to assist with taxon identification. Other citizen science apps do exist. iNaturalist, however, is particularly interesting due to the feature that it allows its users to select from a selection of licenses, of which some are compatible with the licenses upon which content from the Wikimedia family is available. Wikidata uses a CC0 - public - license for its data, Wikipedia is available under a CC-BY-SA and Commons content uses a selection of Creative Commons licenses (Table 1). iNaturalist is also a particular good fit with Wikimedia, because both have a global and multilingual scope. This is a great example of how platforms can support each other's missions by simple policy decisions, such as open licencing, that underpin interoperability.

Percepciones y uso de la Wikipedia en alumnos de educación secundaria

La Wikipedia es un recurso cada vez más usado en los procesos de aprendizaje, aunque generalmente los alumnos sean meros consumidores. Este artículo presenta un análisis de las percepciones que los alumnos de secundaria tienen acerca de la Wikipedia y el uso que hacen de ella. El estudio se realizó con un total de 143 alumnos de fin de Educación Secundaria Obligatoria (15 años) y fin de Bachillerato (17 años) de un centro del ámbito metropolitano de Barcelona. Los resultados sugieren que los alumnos valoran la Wikipedia por su rapidez, facilidad de uso y gran cobertura y aceptan el compromiso con una percibida baja fiabilidad. El estudio también indica una gran diferencia de uso según el ámbito de conocimiento, con prevalencia de uso en materias específicas de investigación y de Humanidades y Ciencias Sociales, y mínimo en el caso de las Matemáticas. Por otro lado, se observa un uso simultáneo de distintas versiones lingüísticas, principalmente catalana y castellana y, en menor medida, inglesa. El artículo contribuye a una mejor comprensión del uso y percepciones de la Wikipedia en alumnos de educación secundaria y pretende servir de base para el diseño de intervenciones educativas centradas en el desarrollo de la competencia digital a través de la edición de la Wikipedia.

Detecting pages to protect in Wikipedia across multiple languages

Wikipedia is based on the idea that anyone can make edits to the website to create reliable and crowd-sourced content. Yet with the cover of internet anonymity, some users make changes to the website that do not align with Wikipedia’s intended uses. For this reason, Wikipedia allows for some pages of the website to become protected, where only certain users can make revisions to the page. This allows administrators to protect pages from vandalism, libel, and edit wars. However, with over five million pages on English Wikipedia, it is impossible for active editors to monitor all pages to suggest articles in need of protection. In this paper, we consider the problem of deciding whether a page should be protected or not in a collaborative environment such as Wikipedia. We formulate the problem as a binary classification task and propose a novel set of features to decide which pages to protect based on (1) users page revision behavior and (2) page categories. We tested our system, called DePP, on four different Wikipedia language versions: English, German, French, and Italian. Experimental results show that DePP reaches at least 0.93 in both AUROC and average precision across the four languages and significantly outperforms baselines. Moreover, DePP works well in a more realistic, unbalanced setting, that is, when unprotected pages are greatly outnumbered by protected pages, by achieving a good AUROC, a high recall and an average precision significantly higher than the baselines in all the settings and languages considered.

Networked Pantheon: a Relational Database of Globally Famous People

This article presents Networked Pantheon, a relational database of biographies of globally famous people spanning the last 5,500 years of human history. This source of information is intended to complement Pantheon 1.0 (Yu et. al. 2016), a biographical dataset that includes temporal, spatial, gender, and occupational information on 11,341 world-renowned people – defined as those who have their biographies in 25 or more Wikipedia language-versions. Networked Pantheon adds information about the biographical links between these historical figures, which was compiled from the hyperlinks between the biographies in English Wikipedia. This digital method allows technics from network analysis to be used to study the relationships between globally famous people, and thus to calculate different measures of historical centrality for individuals, cities, countries, genders, and occupations. Networked Pantheon complements the historical popularity indicators of Pantheon 1.0 with measurements of the centrality of the figures in the network of biographical references, allowing for an approximation to the information flows between different territories, genders, and occupations of famous people over time.

Cross-language article linking with different knowledge bases using bilingual topic model and translation features

Creating links among online encyclopedia articles in different languages is crucial in the construction and integration of large multilingual knowledge bases. Most research to date has focused on linking among different language versions of Wikipedia, yet other large online encyclopedias in a variety of languages exist. In this work, we present a cross-language article-linking method using a bilingual topic model and translation features based on an SVM model to link articles in English Wikipedia and Chinese Baidu Baike, the most widely used Wiki-like encyclopedia in China. To evaluate our approach, we compile data sets from Baidu Baike articles and their corresponding English Wikipedia articles. The evaluation results show that our approach achieves at most 0.8158 in MRR, outperforming the baseline system by 0.1328 (+19.44%) in MRR. Our method does not heavily depend on linguistic characteristics, and it can be easily extended to generate cross-language article links among different online encyclopedias in other languages.

The Most Controversial Topics in Wikipedia: A Multilingual and Geographical Analysis

We present, visualize and analyse the similarities and differences between the controversial topics related to “edit wars” identified in 10 different language versions of Wikipedia. After a brief review of the related work we describe the methods developed to locate, measure, and categorize the controversial topics in the different languages. Visualizations of the degree of overlap between the top 100 list of most controversial articles in different languages and the content related geographical locations will be presented. We discuss what the presented analysis and visualizations can tell us about the multicultural aspects of Wikipedia, and, in general, about cultures of peer-production with focus on universal and specifically, local features. We demonstrate that Wikipedia is more than just an encyclopaedia; it is also a window into divergent social-spatial priorities, interests and preferences.

Analysing Geo-linguistic Dynamics of the World Wide Web: The Use of Cartograms and Network Analysis to Understand Linguistic Development in Wikipedia

Abstract This article discusses the usefulness of geo-linguistic analysis for Internet studies by presenting two techniques to frame and visualize the linguistic development of the World Wide Web, in particular the geo-linguistic development amongst different language versions of Wikipedia. An emergent research agenda has been set to explore the multilingual aspects of the Internet using, for example, a global perspective on Wikipedia research. And yet, there is a lack of theoretical and methodological tools for understanding the distribution and diffusion of linguistic materials online. The idea of geo-linguistic factors is introduced in this article to address these shortcomings and to respond to the study of a wide range of issues such as linguistic pluralism on the Internet or, more generally, the diffusion of innovation. Cartograms and network analysis are presented as two techniques that showcase the potential uses of geo-linguistic analysis. These two techniques of measurement and visualization indicate certain geographic and linguistic affiliations among languages. It is argued that although certain more developed language versions such as English and German may have central positions in connecting all languages, there exists another pattern that can best be explained by geo-linguistic factors. Finally, the limitations and implications of such findings and techniques are discussed, not only for research on Wikipedia but for Internet studies in general.

Conflict and consensus in the Chinese version of Wikipedia

It is not easy to initiate a new language version of Wikipedia. Although anyone can propose a new language version without financial cost, certain Wikipedia policies for establishing a new language version must be followed. Once approved and created, the new language version needs tools to facilitate writing and reading in the new language. Even if a team tackles these technical and linguistic issues, a nascent community has to then develop its own editorial and administrative policies and guidelines, sometimes by translating and ratifying the policies in another language version (usually English). Given that Wikipedia does not impose an universal set of editorial and administrative policies and guidelines, the cultural and political nature of such communities remains open-ended.