Ego Network Data Research Articles

Choosing Your Sports Partners: Assessing Selection Preferences Through Observational and Experimental Studies

ABSTRACT Previous research has demonstrated the positive impact of doing sports together on maintaining physical activity levels. Yet, there remains a gap in understanding the selection processes leading to sports partnerships. We address this question using a two-study design. Study 1 examines longitudinal ego-network data; we analyze data of 1,222 sports partnerships among 409 Dutch university students. We find that emotional closeness, rather than skill similarity, is a key determinant of sports partnership continuation over time. We do not find that the sports setting in which sports partnerships are embedded (i.e. sports clubs, informal groups, commercial gyms, unorganized settings) affect the stability of sports partnerships. Study 2 employs a discrete choice experiment, in which individuals are tasked to choose a sports partner out of choice-sets consisting of three sports partners who vary in the types of motivations and resources they offer (i.e. social comparison, knowledge, companionship, encouragement). People express a strong preference for sports partners who emphasize social aspects of sports, but as individuals become more active, sports partners who emphasize social comparison and who are knowledgeable about sports gain attractiveness. We discuss the implications of these findings for tailored strategies leveraging social networks to promote sports participation.

Nonresponse in name generators across countries and survey modes

Past research indicates interviewer effects lead to an underestimation of network size and higher nonresponse to the “important matters” name generator. Self-administered surveys offer a potential solution, but evidence is mixed and context-specific. We employ a logistic multilevel regression, estimated using a Bayesian Markov Chain Monte Carlo approach, to analyze nonresponse to this name generator from 33 post-electoral surveys across 21 countries in the Comparative National Election Project. We find higher nonresponse in interviewer-administered surveys compared to self-administered surveys, particularly among specific demographic groups. Finally, we discuss the trade-offs in selecting survey modes for collecting ego-network data using this instrument.

Network portfolio diversity and social innovation: An egocentric approach to cross-sector partnerships

The culmination of network and organization research suggests diverse network ties bolster innovation. Extending this line of research, this study examines how three distinct dimensions of network portfolio diversity—duration, tie strength, and collaboration type—are related to organizational innovation. Survey, ego network, and expert evaluation data on 258 U.S. social ventures suggest that these different dimensions have differential impact on the novelty of ideas organizations develop to tackle social issues (i.e., social innovation). Specifically, while diversity in relationship duration with nonprofit partners had an inverted U-shaped curvilinear relationship with social innovation, diversity in collaboration type with business partners and diversity in tie strength with government partners each had a U-shaped curvilinear relationship. These results highlight that the relationship between network diversity and organizational innovation is contingent on the network portfolio dimension, as well as the partner sector. This research contributes to theorizing on social and interorganizational networks, cross-sector partnerships, and social innovation. Findings also have implications for organizational leaders as they configure heterogeneous ego network portfolios to boost innovation for social impact.

The Value of Interpersonal Network Continuity for College Students in Disruptive Times.

The COVID-19 pandemic-related social distancing practices that colleges implemented in Spring 2020 disrupted the typical mechanisms of propinquity (physical proximity) and homophily (shared characteristics) that physical institutions rely on to help students build and maintain relationships critical to learning and wellbeing. To explore how social distancing shaped students' academic and social networks and associated educational outcomes, we conceptualized it as a "network shock" and collected unique ego network data in April 2020. For participating students, maintaining interactions with the same set of individuals before and after social distancing was related to more positive outcomes across a range of self-reported wellbeing and learning indicators. On average, students experienced a loss of frequent academic contacts, while they maintained or replaced social interactions in their interpersonal networks after social distancing. Our investigation of the ways students experienced changes in their social and academic networks after a loss of physical proximity points to the role of interpersonal interaction network continuity for fostering wellbeing and learning in times of disruption, as well as the potential need for support in maintaining or rebuilding academic networks.

Structure of personal networks and cognitive abilities: A study on a sample of Italian older adults

Research in social gerontology has suggested that structural complexity of personal networks could moderate cognitive decline of older adults. In line with the environmental complexity hypothesis, their cognitive functioning would benefit from a high number of cohesive subgroups in their own personal networks, i.e., various social foci, thanks to higher cognitive stimuli from various social interactions. Yet, past studies considered only compositional diversity of networks due to lack of data on alter–alter ties. To fill this gap, we collected survey ego-network data on frequent social contacts (including alter–alter ties) and cognitive functioning on a sample of individuals aged ≥ 75 in Brescia, Italy (N=230). As a proxy for social foci, we detected cohesive subgroups within each respondent’s personal networks. Results showed a positive association between the number of cohesive subgroups and cognitive functioning, regardless of the network size, while controlling for relevant socio-demographic attributes and depression symptoms. Our findings testify to the importance of granular network data in studying the link between social relationships and cognitive functioning.

Ego-networks, SNSs affordances, and personalities: understanding individuals’ selfie posting on SNSs based on Actor-Network Theory

ABSTRACT The prevalence of smartphones and social networking sites (SNSs) has given rise to the popularity of selfie posting, presenting one’s own photograph on SNSs. While previous studies have mainly investigated the effects of personal characteristics (e.g. demographics, personality traits and motivational needs) on selfie posting, the impacts exerted by individuals’ social networks have been largely neglected. Drawing on the Actor-Network Theory, this study explores besides personal traits (i.e. personalities and demographics), how relational characteristics of one’s ego networks (i.e. gender heterogeneity, age homophily, average tie strength, and network density) and SNSs affordances (i.e. connectivity and interactivity) affect selfie posting behavior. Based on a survey sample in which the respondents’ ego network data were collected, individuals’ ego-network metrics were calculated and analyzed. Our analyses show that those whose ego network having a higher proportion of opposite sex (measured by gender heterogeneity) or/and having alters more connected with one another (measured by network density) are more likely to post selfie on SNSs, while those embedded in a strong-tie network (measured by average tie strength) are less likely to post selfie on SNSs. The findings suggest that characteristics of one’s ego network exert important influences on selfie posting on SNSs.

Willingness Maximization for Ego Network Data Extraction in Multiple Online Social Networks

Egocentric network (ego network) data are very important for evaluating algorithms and machine learning approaches in Online Social Networks (OSNs). Nevertheless, obtaining the ego network data from OSNs is not a trivial task. Conventional manual approaches are time-consuming, and sometimes the ego network data are quite incomplete because only a small number of users would agree to provide their data. This is because there are two important factors that should be considered simultaneously for this data acquisition task: i) users' willingness to provide their data, and ii) the structure of the ego network. However, addressing the above two factors to obtain the more complete ego network data has not received much research attention. Therefore, in this paper, we address this issue by proposing a family of new research problems. The first proposed problem, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Willingness Maximization for Ego Network Extraction in Online Social Networks (WMEgo)</i> , identifies a set of ego networks from a single OSN, such that the willingness of the users to provide their data is maximized. We prove that WMEgo is NP-hard and propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{2}(1-\frac{1}{e})$</tex-math></inline-formula> -approximation algorithm, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ego Network Identification with Maximum Willingness (EIMW)</i> . Furthermore, we extend the idea of WMEgo to multiple social networks and formulate a new research problem, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Willingness Maximization on Multiple Social Networks for Ego Network Extraction (WM <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Ego)</i> , which is able to effectively obtain ego network data from multiple social networks <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> . We propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{1}{2}$</tex-math></inline-formula> -approximation algorithm, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>M</u>aximum Expansion for <u>UN</u>ified <u>EX</u>penses (MUNEX)</i> for a special case of WM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Ego and then design a constant-ratio approximation algorithm to the general WM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Ego problem, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Maximum Expansion with Expense Examination (M3E)</i> . We conduct two evaluation studies with 672 and 1052 volunteers to validate the proposed WMEgo and WM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Ego problems, respectively, and show that they are able to obtain much more complete ego network data compared to other baselines. We also perform extensive experiments on multiple real datasets to demonstrate that the proposed approaches significantly outperform the other baselines.

Heatmaps and consensus clustering for ego network exploration

Background: Researchers need visualization methods (using statistical and interactive techniques) to efficiently perform quality assessments and glean insights from their data. Data on networks can particularly benefit from more advanced techniques since typical visualization methods, such as node-link diagrams, can be difficult to interpret. We use heatmaps and consensus clustering on network data and show they can be combined to easily and efficiently explore nonparametric relationships among the variables and networks that comprise an ego network data set. Methods: We used ego network data from the Québec Adipose and Lifestyle Investigation in Youth (QUALITY) cohort used to evaluate this method. The data consists of 35 networks centered on individuals (egos), each containing a maximum of 10 nodes (alters). These networks are described through 41 variables: 11 describing the ego (e.g. fat mass percentage), 18 describing the alters (e.g. frequency of physical activity) and 12 describing the network structure (e.g. degree). Results: Four stable clusters were detected. Cluster one consisted of variables relating to the interconnectivity of the ego networks and the locations of interaction, cluster two consisted of the ego’s age, cluster three contained lifestyle variables and obesity outcomes and cluster four was comprised of variables measuring alter importance and diet. Conclusions: This exploratory method using heatmaps and consensus clustering on network data identified several important associations among variables describing the alters’ lifestyle habits and the egos’ obesity outcomes. Their relevance has been identified by studies on the effect of social networks on childhood obesity.

Better Understanding the Population Size and Stigmatization of Psychologists Using Questionable Research Practices

There has been low confidence in the replicability and reproducibility of published psychological findings. Previous work has demonstrated that a population of psychologists exists that have used questionable research practices (QRPs), or behaviors during data collection, analysis, and publication that can increase the number of false-positive findings in the scientific literature. Across two survey studies, we sought to estimate the current size of the QRP-using population of American psychologists and to identify if this sub-population of scientists is stigmatized. Using a self-report direct estimator, we estimate approximately 18\% of American psychologists have used at least one QRP in the past 12 months. We then demonstrate the use of two additional estimators: the unmatched count estimate (an indirect self-report estimator) and the generalized network scale up method (an indirect social network estimator). Additionally, attitudes of psychologists towards QRP users, and ego network data collected from self-reported QRP users, suggest that QRP users are a stigmatized sub-population of psychologists. Together, these findings provide insight into how many psychologists are using questionable practices and how they exist in the social environment.

Support provided by elderly in Italy: a hierarchical analysis of ego networks controlling for alter-overlapping.

Providing support outside the household can be considered an actual sign of an active social life for the elderly. Adopting an ego–network perspective, we study support Italian elders provide to kin or non–kin. More specifically, using Italian survey data, we build the ego–centered networks of social contacts elders entertain and the ego–networks of support elders provide to other non–cohabitant kin or non–kin. Since ego–network data are inherently multilevel, we use Bayesian multilevel models to analyze variation in support ties, controlling for the characteristics of elders and their contacts. This modeling strategy enables dealing with sparseness and alter–alter overlap in the ego support network data and to disentangle the effects related to the ego (the elder), the dyad ego–alter, the kind of support provided, as well as social contacts and contextual variables. The results suggest that the elderly in Italy who provide support outside their household — compared to all elders in the sample — are younger, healthier, more educated, and embedded in a more diversified ego–network of social contacts. The latter also conveys both the type and the recipient of the support, with the elderly who entertain few relationships with kin being more prone to provide aid to non–kin. Further, a “peer homophily” effect in directing elder support to a non–kin is also found.

Support networks among college enrolled Latinas/os: Using ego networks to examine gender differences in network language use and experiences of discrimination

The growth of the Latina/o population has been met with a backlash that has increased experiences of discrimination. Differences in gender, education, and acculturation often result in experiences of discrimination. Despite the importance of acculturation, few attempts have been made to understand the structural environment surrounding those experiences (i.e., social networks). Using ego network data, the current study examines the relationship between enculturation (a traditional indicator of acculturation), network language use, acculturative pressures, structural social support characteristics, and experiences of discrimination among a college-enrolled sample of Latinas/os (N = 139). Over half of respondents were female (61 %) with ages ranging from 18 to 63 and reported largely first (43 %) and second generational status (27 %). Findings indicate that pressure to acculturate was the strongest variable associated with experiences of discrimination. Network language use moderated by gender emerged over enculturation as more closely related to experiences of discrimination. The interaction term between gender and network language use revealed potentially different risk and protective factors for Latinas/os related to experiences of discrimination. Structural support variables associated with respondents’ social networks appear to provide some protective value but do not attenuate the relationship between pressure to acculturate and experiences of discrimination. In the current study, ego networks proved useful in identifying factors related to experiences of discrimination. Future research should continue to explore the importance of a person’s immediate social structure (i.e., ego networks) in protecting and exposing Latinas/os to harmful and health benefiting experiences.

Dutch without the Dutch: discourse, policy, and program impacts on the social integration and language acquisition of young refugees (ages 12–23)

ABSTRACT This innovative mixed methods network study with young refugees in the Netherlands disproves integration policy assumptions that language acquisition is key to social integration and that social integration is about gradually increasing contact with native citizens, or when integration is lacking, about limiting contact to one’s own ethnic group. The study connects the research approaches of government-commissioned quantitative integration studies, which generally reflect the above assumptions, and qualitative studies with migrants, which reveal an ‘integration paradox’ and a ‘doubleness’ of language acquisition generating both inclusion and exclusion. First, it includes the use of languages and countries of origin in ego network data to examine the relationship between language acquisition and social integration. These network data show that participants speak Dutch with most of the diverse non-Dutch people in their networks and have relatively few connections with Dutch natives. Second, to help us understand these findings, participants were asked to share their ‘network stories’. These stories place the network findings within the context of contradictory asylum and integration policies and negative discourse, thereby revealing that with regard to learning Dutch, participants are caught not in a paradox but in an aporia: damned if they do, damned if they don’t.

Estimating Contextual Effects from Ego Network Data.

Network concepts are often used to characterize the features of a social context. For example, past work has asked if individuals in more socially cohesive neighborhoods have better mental health outcomes. Despite the ubiquity of use, it is relatively rare for contextual studies to employ the methods of network analysis. This is the case, in part, because network data are difficult to collect, requiring information on all ties between all actors. This paper asks whether it is possible to avoid such heavy data collection while still retaining the best features of a contextual-network study. The basic idea is to apply network sampling to the problem of contextual models, where one uses sampled ego network data to infer the network features of each context, and then uses the inferred network features as second-level predictors in a hierarchical linear model. We test the validity of this idea in the case of network cohesion. Using two complete datasets as a test, we find that ego network data are sufficient to capture the relationship between cohesion and important outcomes, like attachment and deviance. The hope, going forward, is that researchers will find it easier to incorporate holistic network measures into traditional regression models.

That’ll move the chains: Collecting network chain data

Although most network studies involve the collection of either ego or whole network data, a smaller subset of work has focused on the collection of network chain data. Collecting network chain data involves collecting a path in an unobserved whole network, and can be useful for capturing phenomena like degrees of separation or search processes. In this paper, we draw on past network chain data collection studies and reviews to propose a design framework for network chain data collection. Next, we use this framework to describe the qualitative collection of network chain data from a pilot sample of public school educators, and the quantitative collection of network chain data from a statewide sample of 600 public school principals and superintendents. Drawing on lessons learned from these data collection efforts, we discuss specific data collection strategies for improving the quality of network chain data, reflecting on what worked and what didn’t, offering recommendations for future studies involving network chain data collection.

Dynamic Credit Quality Evaluation with Social Network Data

We investigate the filtering problem where the borrower’s time varying credit quality process is estimated using continuous time observation process and her (in this paper we refer to the borrower as female and the lender as male) ego-network data. The hidden credit quality is modeled as a hidden Gaussian mean-reverting process whilst the social network is modeled as a continuous time latent space network model. At discrete times, the network data provides unbiased estimates of the current credit state of the borrower and her ego-network. Combining the continuous time observed behavioral data and network information, we provide filter equations for the hidden credit quality and show how the network information reduces information asymmetry between the borrower and the lender. Further, we consider the case when the network information arrival times are random and solve stochastic optimal control problem for a lender having linear quadratic utility function.

Credit Scoring with Ego-Network Data

This article investigates a stochastic filtering problem whereby the bor-rower’s hidden credit quality is estimated using ego-network signals. The hidden credit quality process is modeled as a mean reverting Ornstein-Ulehnbeck process. The lender observes the borrower’s behavior modeled as a continuous time diffusion process. The drift of the diffusion process is driven by the hidden credit quality. At discrete fixed times, the lender gets ego-network signals from the borrower and the borrower’s direct friends. The observation filtration thus contains continuous time borrower data augmented with discrete time ego-network signals. Combining the continuous time observation data and ego-network information, we derive filter equations for the hidden process and the properties of the conditional variance. Further, we study the asymptotic properties of the conditional variance when the frequency of arrival of ego-network signals is increased.

Network Locations or Embedded Resources? The Effects of Entrepreneurs’ Social Networks on Informal Enterprise Performance in Ethiopia

Unlike the industrialized economies where economic exchange is mainly based on impersonal transactions, African economies rely heavily on interpersonal relationships and social networks. Though the study of social networks in African economies is not new, quantitative perspectives on returns of social networks in the informal sector have rarely been the subject of empirical analysis. While studies in Ethiopia have documented how rural households use their networks to improve agricultural productivity and livelihoods, little is known about the contribution of networks in the informal economy. Yet, debates are prevalent on the contribution of social networks in enterprise performance as their effects vary depending on the context of study. This paper thus examines the effects of entrepreneurs’ social networks on informal enterprise performance. Multi-stage sampling procedures involving purposive and systematic random-walk techniques were employed to draw samples. Ego-network data were collected through name generator and interpreter surveys constructed on the basis of regular relations of people related to resources needed and obtained by entrepreneurs for the operation of businesses. The data were analyzed using social network analysis and statistical procedures. Multiple regression models were used to investigate the extent to which an entrepreneurs’ location in networks and embedded resources affect enterprise performance. The findings revealed that while the diversity of contact resources has a significant positive effect on enterprise performance, an entrepreneurs’ location in a network has a significant negative effect.

The Role of Ego Networks in Compulsive Exercise Behavior Among a Sample of College Sorority Women.

Compulsive exercise (CE) is a harmful form of exercise that elevates the risk of developing/sustaining clinical eating disorders. College-aged sorority women are especially prone to CE. Due to the pronounced impact social relationships have on college students' behavior, this study aims to examine personal networks and CE among a sample of sorority women through an egocentric network analysis. A total of 204 women in a sorority from a large, private university in the southeastern United States completed a cross-sectional survey in spring 2015. Descriptive and regression analyses were conducted on demographic, attribute, and ego network data. Relationships with siblings, significant others, and roommates were protective against CE in this sample. Conversely, body dissatisfaction and exercise frequency predicted CE. Findings suggest that social relationships can impact CE behaviors in this sample. Along with promoting body satisfaction and healthy exercise, public health efforts should focus on facilitating close interpersonal relationships, especially between sorority women and siblings, significant others, and roommates.

Using Ego Network Data to Inform Agent-based Models of Diffusion

Agent-based modeling holds great potential as an analytical tool. Agent-based models (ABMs) are, however, also vulnerable to critique, as they often employ stylized social worlds, with little connection to the actual environment in question. Given these concerns, there has been a recent call to more fully incorporate empirical data into ABMs. This article falls in this tradition, exploring the benefits of using sampled ego network data in ABMs of cultural diffusion. Thus, instead of relying on full network data, which can be difficult and costly to collect, or no empirical network data, which is convenient but not empirically grounded, we offer a middle-ground, one combining ABMs with recent work on network sampling. The main question is whether this approach is effective. We provide a test of the approach using six complete networks; the test also includes a range of diffusion models (where actors follow different rules of adoption). For each network, we take a random ego network sample and use that sample to infer the full network structure. We then run a diffusion model through the known, complete networks, as well as the inferred networks, and compare the results. The results, on the whole, are quite strong: Across all analyses, the diffusion curves based on the sampled data are very similar to the curves based on the true, complete network. This suggests that ego network sampling can, in fact, offer a practical means of incorporating empirical data into an agent-based model.

Между храмом культуры и торговым центром: новые институции в структуре поля художественных организаций Санкт-Петербурга

&#x0D; Мария Андреевна Сафонова – кандидат социологических наук, доцент, Департамент социологии, Национальный исследовательский университет «Высшая школа экономики» (Санкт-Петербург). Адрес: 190008, Санкт-Петербург, ул. Союза Печатников, д. 16. E-mail: msafonova@hse.ru&#x0D; Надежда Александровна Соколова – магистрант, факультет политических наук и социологии, Европейский университет в Санкт-Петербурге. Адрес: 191187, Санкт-Петербург, ул. Гагаринская, д. 3а. E-mail: nsokolova@eu.spb.ru&#x0D; Александра Сергеевна Бармина – магистрант, Департамент социологии, Национальный исследовательский университет «Высшая школа экономики» (Санкт-Петербург). Адрес: 190008, Санкт-Петербург, ул. Союза Печатников, д. 16. E-mail: barminaaa@gmail.com&#x0D; Цитирование: Сафонова М.А., Соколова Н.А., Бармина А.С. (2017) Между храмом культуры и торговым центром: новые институции в структуре поля художественных организаций Санкт-Петербурга // Мир России. Т. 26. № 4. С. 143–168. DOI: 10.17323/1811-038X-2017-26-4-143-168&#x0D; &#x0D; Статья посвящена развитию организационной популяции учреждений культуры в Санкт-Петербурге в 2000-х годах. В этот период государство вновь начинает активно финансировать учреждения культуры и при этом постепенно переходит к неолиберальной культурной политике, возрождются практики корпоративного и индивидуального спонсорства, увеличиваются доходы образованных жителей крупных городов. Эти процессы открывают ресурсные ниши, инициируя тем самым и появление новых художественных организаций (в том числе негосударственных) и создание новых организационных форм. Однако институциональная легитимность негосударственных организаций остается на сегодняшний день проблематичной. Для того чтобы понять, почему их деятельность не является полностью легитимной и каковы последствия отсутствия легитимности, было проведено исследование, основанное на сочетании качественных и количественных методов сбора и анализа данных. Мы обнаруживаем, что возникают два типа новых культурных организаций. Организации первого типа имеют стабильную финансовую поддержку от частных спонсоров, что обеспечивает им возможность фокусироваться на выставочной и/или просветительской деятельности и отказываться от коммерческой. Организации второго типа возникают как низовые инициативы без стабильной частной или государственной поддержки. Поэтому они совмещают художественную и коммерционализированную активности; коммерционализированная активность делает их художественную деятельность нелегитимной в глазах агентов из старых государственных организаций и новых организаций, имеющих стабильную финансовую поддержку. Вопреки теории П. Бурдье, новые организации не создают символически доминирующий авангард; вопреки тезисам Н. Флигстина и Д. МакАдама, их роль не характеризуется как роль подчиненных, но готовых к восстанию претендентов. В ситуации устоявшегося организационного истеблишмента и стойкой легитимности государственной поддержки низовые культурные инициативы оказываются в ситуации порочного круга: они не могут стать финансово устойчивыми, пока не стали более легитимными и не нашли спонсоров, но при этом проигрывают в борьбе за спонсоров, пока не получили легитимный статус, и вынуждены получать ресурсы за счет коммерческой деятельности, которая осуждается в данном поле.