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Digital divide and digital capital in multiethnic Russian society


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The paper draws linkages between ethnic diversity of the eight federal districts of Russia and their technological development (access and use of ICTs, digital literacy, etc.). We show that although there is no universal correlation between ethnic composition of the regions and the level of their technological advancement, regions where Russians constitute the majority (i.e. Central and Northwestern) more often tend to be the country's leaders in terms of technological development. Following up on this, we use purposive sample of 398 Internet users based in Russia, showing how the level of digital capital of users varies depending on their ethnicity (here we will distinguish between two large groups – Russians and non-Russians, based on self-identification of survey participants) and their place of living. Results of the digital capital study, despite being indicative, show that those belonging to the ethnic majority (in our case Russians) and those living in big cities tend to have a higher level of digital capital. We argue that although ethnicity solely does not define the level of users' digital capital, it is still an important and understudied issue. This is particularly true for big multiethnic societies, such as the Russian society, where digital divide across various groups and regions remains a serious problem.
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Journal of Multicultural Discourses
ISSN: 1744-7143 (Print) 1747-6615 (Online) Journal homepage:
Digital divide and digital capital in multiethnic
Russian society
Anna Gladkova, Elena Vartanova & Massimo Ragnedda
To cite this article: Anna Gladkova, Elena Vartanova & Massimo Ragnedda (2020): Digital divide
and digital capital in multiethnic Russian society, Journal of Multicultural Discourses
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Published online: 30 Mar 2020.
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Digital divide and digital capital in multiethnic Russian society
Anna Gladkova
, Elena Vartanova
and Massimo Ragnedda
Faculty of Journalism, Lomonosov Moscow State University, Moscow, Russia;
Department of Arts,
Northumbria University, Newcastle upon Tyne, UK
The paper draws linkages between ethnic diversity of the eight
federal districts of Russia and their technological development
(access and use of ICTs, digital literacy, etc.). We show that
although there is no universal correlation between ethnic
composition of the regions and the level of their technological
advancement, regions where Russians constitute the majority (i.e.
Central and Northwestern) more often tend to be the countrys
leaders in terms of technological development. Following up on
this, we use purposive sample of 398 Internet users based in
Russia, showing how the level of digital capital of users varies
depending on their ethnicity (here we will distinguish between
two large groups Russians and non-Russians, based on self-
identication of survey participants) and their place of living.
Results of the digital capital study, despite being indicative, show
that those belonging to the ethnic majority (in our case Russians)
and those living in big cities tend to have a higher level of digital
capital. We argue that although ethnicity solely does not dene
the level of usersdigital capital, it is still an important and
understudied issue. This is particularly true for big multiethnic
societies, such as the Russian society, where digital divide across
various groups and regions remains a serious problem.
Received 15 December 2019
Accepted 14 March 2020
Digital divide; digital
inequalities; digital capital;
ethnic groups; Russia
Russian Federation is a unique example of a multiethnic and multicultural nation, with a
total population of 146 million people, including over 190 ethnic groups speaking over
170 languages (most recent data provided by Vserossiiskaya perepis naseleniya 2010). It
is also the biggest country in the world, spreading over 11 time zones and having a terri-
tory of over 17,100,000 square km. Russia consists of eight federal districts divided into 85
federal subjects, 22 out of which are national republics within Russia (Vserossiiskaya
perepis naseleniya 2010). Some entities within Russian Federation have their own titular
nations dominant ethnic groups, typically after which the region was named (e.g. the
Republic of Tatarstan, the Republic of Bashkortostan, Chuvash Republic, etc.). Russian
regions dier from each other economically (e.g. average salaries rate, GDP, size and
eciency of economy, etc.), geographically (e.g. territorial dierences, distance from the
large cities and the two main megapolises, Moscow and St. Petersburg, etc.),
© 2020 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Anna Gladkova Faculty of Journalism, Lomonosov Moscow State University,
Moscow, Russia
technologically (uneven connection of Russia by transportation and ICT infrastructures,
rst and foremost due to the unprecedented scale of the country), socially (population
density, size of urban/rural population, dierences in education, opportunities on labor
market, etc.), as well as ethnically and linguistically (e.g. the number of smaller ethnic
and cultural groups residing in particular districts of Russia).
Cross-regional varieties and contrasts become evident when it comes to the analysis of
inequalities in Russia. Previous research on Russia showed that due to its complex and
immense territory, its economic and cultural development, historical traditions, pro-
fessional journalistic practices, policy measures, legislation, uneven economic develop-
ments of the regions, even geographical and climatic conditions (Dunas 2013; Svitich,
Smirnova and Shkondin 2018; Zorin 2018; Vartanova 2009,2014,2015), Russia represents
an interesting case for the analysis of dierent kinds of inequalities. In this vein, Vartanova
(2002) discussed a correlation between social changes, transition from socialism to market
and democracy on the one hand, and technological revolution on the other hand, talking
about dual transitionin post-socialist context (Vartanova 2002: 450). Later, scholars
focused on inequalities in socioeconomic development of the Russian regions (Kolomak
2010); inequalities in access to the higher education in dierent parts of the country
(Mikheeva 2004); and inequalities in the quality of life in Russia (Bobkov, Gulyugina, and
Odintsova 2009).
Fewer research, however, has been conducted to investigate the development of
digital inequalities in the context of the Russian transition after dissolution of the Soviet
Union (Vartanova 2002; Deviatko 2013; Nagirnaya 2015; Volchenko 2016), regardless the
fact that the problem of digital divide plays an important role for hindering the develop-
ment of the civil society(Rykov, Nagornyy, and Koltsova 2017: 70). So far, most publi-
cations on the digital divide in Russia (Vartanova 2018; Volchenko 2016; Bykov and Hall
2011; Deviatko 2013) have had rather general character. They discuss digital inequalities
in regard to digital economy and/or information society issues in the transitional post-
socialist context, aim to conceptualize the notion of the digital divide and classify theor-
etical approaches to it, from pure access problem to a broader social one (Gladkova, Gar-
ifullin, and Ragnedda 2019a). The majority of papers on the digital divide in Russia
approach it mostly from a technological point of view, i.e. a divide between those who
access and those who are excluded from the digital world and discuss a multitude of
factors that can inuence that divide.
In this vein, Bykov and Hall (2011) discuss how the age and education level inuence
the access to the Internet in Russia, while Brodovskaya and Shumilova (2013) note a cor-
relation between the region of living, the distance from the city centre and the intensity of
Internet use. Volchenko (2016) underlines correlations between age, gender, level of
income and education, region of living and overall involvement of respondents into
digital environment. Zherebin and Makhrova (2015) show that the time people spend
online varies depending on their age. A number of papers approach digital inequalities
in a broader inter-regional perspective, analyzing and comparing regions of Russia by
the level of Internet penetration, speed, cost, etc. (Deviatko 2013; Nagirnaya 2015),
while again mostly discussing the problem of access/lack of such and factors that
can inuence it.
What is missing in previous research on this topic is a study of digital inequalities in
Russia in regard to multiethnic and multicultural character of the Russian society, which
is in fact quite unique compared to many other countries of the world. Given unprece-
dented number of ethnic and cultural groups living in Russia, an important role of
digital technologies in securing ethno-cultural diversity, pluralism and inclusion (Matsaga-
nis, Katz, and Ball-Rokeach 2011), and the fact that minor ethnic groups are often under-
represented in Russian online space due to a number of reasons technological, literacy,
motivation related, etc. (Gladkova and Korobeinikova 2016), we believe a study of digital
divides in relation to ethnicity factor in Russia is long overdue.
In this paper, we will approach multiculturalism and ethnicity in Russia through the lens
of Cultural Discourse Studies, arguing that culture is integral part of the life practice of a
social community in relation to others, complex and dynamic, rather than xed to
people, place or time (Shi-xu 2016: 2). We will show that ICTs and Internet studies are
no longer limited to the technology domain, becoming important topics for communi-
cation/discourse/cultural studies too (Dutton and Reisdorf 2019). In this vein, following
Shi-xu (2016) we will show how digital means enable us to identify, characterize,
explain, interpret and appraise culturally divergent, productive or competing discourses
(Shi-xu 2016: 3). Furthermore, we believe our research contributes to academic discussion
about re-conceptualization of Eastern and Western paradigms of cultural discourse
studies (Shi-xu 2009), and about the role culture plays in social transition of Emerging
States, beyond the traditional role of politics and market interplay (Ekecrantz 2007). We
agree the diversity of cultures, including intellectual cultures, is inevitable and good for
humanity and yet that all cultural intellectual traditions should hold the same basic
value of mutual respect and strive for common human wellbeing (Ekecrantz 2007: 41).
This is particularly true for multicultural and multiethnic Russian society, where diverse
cultural, linguistic and ethnic groups with dierent historical traditions and backgrounds
co-exist, and where multiculturalist stance (Shi-xu 2006)on state and public levels
plays a very important role.
Research questions we will be addressing in this paper are the following ones: rst,do
Russian regions dier in terms of access and use of ICTs when it comes to the ethnic com-
position of these regions? Here we are interested in nding out any possible correlations
between the number of ethnic groups and languages spoken in the region, as well as their
size compared to Russians living there, and technological advancement of that region
(based on measurable indicators such as Internet audience numbers, cost and speed of
connection, etc.). Second, is there any correlation between the second level of the
digital divide and ethnic belonging of users? Here we would like to see if Russian and
non-Russian Internet users (i.e. those belonging to other ethnic groups)
dier anyhow
in terms of their digital literacy and competences. Third, is there any correlation
between digital capital dened by Ragnedda (2018)asa set of internalized ability and
aptitude(digital competencies) as well as externalized resources(digital technology)
and ethnicity and place of living in Russia?
To answer these questions, we will rst look at the multiethnic character of the
Russian federal districts and show which of the eight districts are most ethnically
diverse by comparing the size of Russians and non-Russian ethnic groups living
there. We will then attempt to draw linkages between ethnic diversity of the federal dis-
tricts and their technological development in regard to access and use of ICTs, including
digital literacy index for each district. Following up on this, we will show how the level of
digital capital of user varies depending on their ethnicity (here we will distinguish
between two basic groups Russians and non-Russians, again relying on self-identi-
cation of survey participants) and place of living. Consequently, we will discuss
whether dierences (if any) in digital access and use, as well as digital capital
between major and minor ethnic group can lead to social inequalities in the society
today, where some communities become less advantaged as a result of digital divide
(Ragnedda 2017). As we will explain later when talking about digital capital, though,
we do not believe that ethnicity itself determines the level of digital capital, since
there is a bundle of other factors that may play a role here (social status, education,
region of living, economic wealth, etc.). Still, we assume that in this broader context
the level of digital capital may vary across ethnic groups, contributing or hindering
such processes as self-accentualisation or self-determination (language and culture
revitalization, access to economic opportunities, etc.), which are particular important
for minor groups in a complex multicultural setting.
In our study, we will be using the theoretical and empirical model elaborated by Rag-
nedda and Ruiu (2020) to measure the level of digital capital. In fact, while the concept of
digital capital is not new (Tapscott, Lowy, and Ticoll 2000) and it has been used in several
dierent way (Seale, Ziebland, and Charteris-Black 2006; Roberts and Townsend 2015),
Ragnedda & Ruius approach not only oers a nuanced theoretical concept based on
the Bourdieusian approach, but above all an empirical tool to measure the individual
levels of digital capital. In this way, we will be applying this model to the Russian
context, giving a specic focus on the ethnicity. This research is the rst attempt ever to
implement to concept of digital capital and its theoretical and empirical model in Russia,
using a purposive sample of Internet users. We will conclude by discussing results of the
study based on both secondary and primary datasets and arguing that for the most eth-
nically diverse regions of Russia bridging digital divide is particularly important to ensure
inclusion and pluralism in public space. All in all, we believe that this study will contribute
both to better understanding of the digital divide in Russia as a social and technological
problem in general, and in regard to multiethnic character of the Russian society in
To put our study into a broader context, it should rst be noted that Russia has historically
been a multiethnic nation. The last ocial Soviet census, conducted in 1989, listed more
than 100 nationalities. At that time, Russians constituted 81.5 percent of the population,
with the next-largest groups being Tatars (3.8 percent), Ukrainians (3.0 percent),
Chuvash (1.2 percent), Bashkirs (0.9 percent), Belorussians (0.8 percent), and Mordovians
(0.7 percent). Other groups totaling more than 0.5 percent of the population each were
Armenians, Avars, Chechens, Germans, Jews, Kazaks, Mari, and Udmurts. In 1992, an esti-
mated 7.8 million people native to the other fourteen former Soviet republics were living
in Russia.
According to the latest all-Russia census of 2010, there are over 190 ethnic groups at
the territory of the Russian Federation speaking more than 170 languages today.
Among the biggest ethnic groups, except for ethnic Russians are Tatars (3.8 percent),
Bashkirs (1.15 percent), Chuvash (1.05 percent) and Chechens (1.04 percent). Besides
the Slavs (Russians, Ukrainians, Belarusians), the big groups include Altaic group, with
mainly speakers of Turkic languages widely distributed in the middle Volga, the
Southern Ural Mountains, the North Caucasus, and above the Arctic Circle. The main
Altaic peoples in Russia are the Balkars, Bashkirs, Buryats, Chuvash, Dolgans, Evenks,
Kalmyks, Karachay, Kumyks, Nogay, and Yakuts. Other big groups are Uralic group, con-
sisting of Finnic peoples living in the upper Volga, the far Northwest, and the Urals,
includes the Karelians, Komi, Mari, Mordovians, and Udmurts. The Caucasus group is
concentrated along the Northern slopes of the Caucasus Mountains; its main subgroups
are the Adyghs, Chechens, Cherkess, Ingush, and Kabardins, as well as about thirty Cau-
casus peoples collectively classied as Dagestani.
Many of abovementioned ethnic groups have been historically living at the territory of
the Russia/former Soviet and/or Russian Empire territories (Tatars, Bashkirs, Yakuts, Buryats
and others) and do not have separate territorial formations outside Russia. Other big
ethnic groups (Ukrainians, Koreans and others) have own territorial formations outside
Russia, although many of them have been living in the country for a very long time too.
Numerous ethnic groups are dispersed through eight federal districts of the Russian Fed-
eration (see Figure 1). Central federal district where Moscow is located is the biggest
federal district by population (39.2 mln people). It is followed by the Volga federal district
(29.6 mln people); Siberian (19.3 mln people); Southern (16.4 mln people); Northwestern
(13.8 mln people), where the second biggest city in Russia, St. Petersburg is located;
Ural (12.3 mln people); North Caucasus (9.7 mln people); and Far Eastern (6.1 mln
people) (Chislennost naseleniya federalnykh okrugov Rossii 2017) are the least populated
regions of Russia.
Today, Russians are not only the biggest ethnic group in the country in general but also
the biggest ethnic group (around or over 90 percent) in most federal subjects (i.e. national
republics, oblasts, okrugs, etc.) included into eight federal districts of Russia. To illustrate
Figure 1. Federal districts of the Russian Federation (1 Central; 2 Northwestern; 3 Volga; 4
Southern; 5 North Caucasus; 6 Ural; 7 Siberian; 8 Far Eastern). Source: our elaboration.
this: Russians for example constitute 95.5 percent of the total population in Voronezh
oblast (Central federal district), with the second biggest ethnic group there being Ukrai-
nians (1.9 percent), and 91.6 percent in Moscow (same federal district), with the second
biggest ethnic groups there being Ukrainians and Tatars (1.4 percent each).
Central federal district appears to be the only federal district in Russia where Russians
are a dominant ethnic group in all territorial entities included into that district. This
becomes particularly clear if we look at some national republics in North Caucasus area
or some national republics within Volga federal district (see Table 1).
Table 1. Ethnic composition of the population of the Russian federal districts (2010).
Federal subject of the Russian Federation
Russians (in percent of the
total population
of the region, i.e. 100 percent)
Other big ethnic groups
(in percent of the total population
of the region, i.e. 100 percent)
Northwestern federal district
Republic of Karelia 82.2 Karels, 7.4
Republic of Komi 65.5 Komi, 23.7
Nenets autonomous okrug 66.1 Nenets, 18.6
Southern federal district
Astrakhan oblast 67.6 Kazakh, 16.3
Republic of Adygeya 63.6 Adygs, 25.2
Republic of Kalmykia 57.4 Kalmyks, 30.2
Volga federal district
Saratov oblast 87.6 Kazakh, 3.1
Perm krai 87.1 Tatars, 4.6
Penza oblast 86.8 Tatars, 6.4
Samara oblast 85.6 Tatars, 4.1
Orenburg oblast 75.9 Tatars, 7.6
Ulianovsk oblast 73.6 Tatars, 12.2
Udmurt Republic 62.2 Udmurts, 48
Republic of Mordovia 53.4 Mordva, 40
Republic of Mary El 47.4 Mari, 43.9
Republic of Tatarstan 39.7 Tatars, 53.2
Republic of Bashkortostan 36.1 Bashkirs, 29.5
Chuvash Republic 26.9 Chuvash, 67.6
Ural federal district
Chelyabinsk oblast 83.8 Tatars, 5.4
Tuymen oblast 73.3 Tatars, 7.5
Khanty-Mansi autonomous okrug Yugra 68.1 Tatars, 7.6
Yamalo Nenets autonomous okrug 61.7 Ukrainians, 9.7
Siberian federal district
Omsk oblast 85.8 Kazakhs, 4.1
Republic of Khakasia 81.7 Khakas, 12.1
Republic of Buryatia 66.1 Buryats, 30
Altai Republic 56.6 Altais, 33.9
Republic of Tyva 16.3 Tyva, 82
Far Eastern federal district
Sakhalin oblast 86.5 Koreans, 5.3
Kamchatka krai 85.9 Ukrainians, 3.9
Magadan oblast 84.1 Ukrainians, 6.5
Chukotka autonomous okrug 52.5 Chukchi, 26.7
Republic of Sakha (Yakutia) 37.8 Yakuts (Sakha), 49.9
North Caucasus federal district
Republic of Karachaevo-Cherkessiya 31.6 Karachaevs, 41
Republic of North Osetia Alania 20.8 Osetins, 65.1
Republic of Kabaldino-Balkariya 22.5 Kabardins, 57.2
Republic of Dagestan 3.6 Avars, 29.4
Chechen Republic 1.9 Chechens, 95.3
Republic of Ingushetia 0.8 Ingush, 94.1
Source: Federal state statistics service. Our elaboration.
This data shows proportions in ethnic composition of the federal subject, comparing
the size of Russian and the second biggest non-Russian ethnic group. However, we
should keep in mind that the actual ethnic composition of the Russian regions is much
more complex, since in each federal subject there are multiple smaller ethnic groups
not listed here: for example, there are over 30 ethnic groups living in the Republic of Dage-
stan only in addition to Avars (29.4 percent) and Russians (3.6 percent) there are Dargins,
Kumyks, Lezgins, Laktz, constituting from 17 to 5 percent of the population each, and
many others (Chislennost naseleniya federalnykh okrugov Rossii 2017).
The challenge here is that many indigenous groups are traditionally based in regions
that are less economically advantaged (cf. Central and North Caucasus federal districts
for example), therefore experiencing problems with digital access and digital use.
Immense ethno-cultural heterogeneity proves to be indisputably challenging when it
comes to securing pluralism and diversity in Russian cyberspace. Research showed that
many ethnic groups in Russia have limited access to ICTs and Internet or/and quite
often lack digital competences and skills to be able to use advanced technologies, there-
fore being subject to the rst (access to the Internet and ICTs) and the second (digital lit-
eracy and use) levels of the digital divide (Gladkova, Garifullin, and Ragnedda 2019a;
Gladkova et al. 2019b). Those who belong to the ethnic groups residing outside their ter-
ritorial formations or do not have a specic formation within the Russian Federation face
particular diculties in ensuring access to electronic media in their own languages
(Protsyk and Harzl 2013: 48). All these factors make Internet an indisputably important
medium and platform for communication for Russian people from dierent cultural and
social backgrounds. Furthermore, providing all ethnic groups access to ICTs means provid-
ing them equal opportunities for development, for reaching their target audience and
airing their diverse views and interests in public. Pluralism in cyberspace also supports
access of all citizens to a wide spectrum of cultural representations, values and opinions
of diverse communities, thus broadening ones cultural horizons and encouraging
people to approach things in a dierent way. Last but not least, pluralistic online media
environment is one of fundamental contributors to building a multicultural society,
where interests and cultural identities of all members of the society are equally respected
and protected.
Materials and methods
In order to give an answer to our research questions, this study is using both secondary
and primary data. In this way, we aim to oer both a macro and micro analysis of
digital inequalities in Russia, by rst providing an overview of the digital divide in
Russia and then digging deeper analyzing dierent levels of digital capital by using a pur-
posive sample. In this way, macro and micro analyses complement each other.
More specically, in order to provide a broad overview of the digital inequalities in
Russia, we use data of the global, national (e.g. the total number of daily Internet users
in Russia) and more regionalized character (related to federal districts of Russia) to
provide an overview of digital inequalities in the whole country. In particular, we will
use data from the Federal State Statistics Service (2018) and reports prepared by
Rossiya Segodnya as part of the RIA Reiting project. In regard to the Internet penetration
and spread of technologies, we will use various reports on the development of the Internet
and ICTs in Russia prepared by the World Bank Group, GfK (2018), Mediascope (2018),
Public Opinion Fund (20172018), Yandex (2016), Internet World Stats (20172018) and
We Are Social/Hootsuite (2019). Furthermore, to shed light upon the second and the
third levels of the digital divide, we will discuss digital literacy report on Russia (2018)
developed by ROCIT. As previous research shows (Volchenko 2016; Chernova, Zobov,
and Starostin 2019; Vartanova and Gladkova 2019), these organizations and reports are
valid for the analysis of digital access and use in Russia and are therefore often used in
similar studies, where data from open sources are provided.
For the study of relations between the level of digital capital and ethnicity, and also
between Internet use and ethnicity we are using a model developed by Ragnedda and
Ruiu (2020) and tested on a representative sample of UK population (Ragnedda, Ruiu,
and Addeo 2019) to see if ethnic belonging of users or their place of living in Russia are
anyhow related to their digital competences and use of ICTs.
It should be made clear here that when speaking about ethnicity we do not treat it as a
self-evident category or independent variable but rather as a category closely linked to
other factors (language, culture, socio economic prole of the region). For the ethnic
groups based in Russia, native language is often a crucial factor when it comes to
dening their ethnic belonging. The same counts in some way for the region of living,
which can be related to self-identication of people as representatives of a particular
ethnic group, specically if we talk about national republics within Russian Federation,
which have several ocial languages in public use. That is why in this study we look at
ethnicity alongside with the survey participantsplace of living and other factors not
covered in this paper. To make it clear, we do not believe that any of the ethnic groups
is likely to have higher level of digital capital simply because of its ethnic belonging: it
is rather a mixture of dierent factors (education, socio-economic status, region of living
and local digitalization policy there, etc.) that aects nal result here.
For this study, we used a purposive sampling of 398 Internet users who were asked
about their ethnic belonging and place of living (in addition to other socio-demographic
variables) when lling in online questionnaire in summer 2019. The online survey was sub-
mitted via emails by using professional and personal contacts across the country. The
survey, originally developed by Ragnedda, Ruiu, and Addeo (2019) has been re-adapted
to the Russian context. The survey was pilot tested upon 20 Internet users over two
rounds. Amendments were made based on the feedback provided. The nal sample we
analyzed in regard to the digital capital was the following one (Table 2).
Table 2. Sample description.
Count %
Russian 308 77.4
Non Russian 90 22.6
Total 398 100.0
Place of living
Count %
in a city with population between 250 and 500 thousand people 47 11.8
in a city with population between 500 thousand and 1 million people 64 16.1
in a city with population less than 250 thousand people 55 13.8
in a city with population over 1 million people 216 54.3
village 16 4.0
Total 398 100.0
Although the sample is at this stage not representative for the whole population of the
country, we decided to pilot test the model to see if it can reveal any trends that we expect
to analyze in future works, using a larger representative sample. Following the above-men-
tioned model, we have calculated the Digital Capital Index (Figure 2), by combining the
two sub-Indexes represented by Digital Access and Digital Competence (Tables 3 and 4).
The rst Index, related to Digital Access, was created by using a Factor Analysis (FA) to
extract a single factor from the four variables included in the rst sub-component, namely
(a) digital equipment (.770), (b) support and training (.433), (c) time spent online (.649), and
(d) connectivity (.520) (Table 5).
On the other hand, the Digital Competence Index was calculated by asking respondents
to self-assess their Digital Competence on a ve-point Likert-type, on ve areas of compe-
tence: (a) information and data literacy, (b) communication and collaboration, (c) digital
content creation, (d) safety and (e) problem solving. We then used the FA to extract a
single factor from these ve variables. Table 6 shows that safety (.815), problem solving
(.791) and content creation (.753) positively contribute to this component.
These three factors were transformed into three dierent variables that were combined
into the Digital Competence Index by repeating the FA and extracting a unique factor.
Finally, by using FA we combined the two indexes into the Digital Capital Index (DCI),
and we extracted a single factor representing the DCI (Table 7).
As last step, we converted the result of this FA to a range from 0 to 100 to simplify its
understanding. The DCI was used as a single variable to investigate any relations with eth-
nicity and geographical residences of users, in order to provide a more specic analysis
and insights about the level of digital inequalities in Russia.
Results and discussion
This session is split into two parts. In the rst part, based on secondary data collected on
national level, we will analyze digital inequalities in the Russian Federation, by focusing on
Figure 2. Constitutive components of the Digital Capital. Source: Ragnedda and Ruiu (2020): 42.
its multiethnic character. In the second part we will present and discuss the results of the
Digital Capital Survey submitted to a purposive sample of Russian users, to stress the
dierent level of digital capital (if any) among dierent ethnic groups.
Macro level: digital inequalities in multiethnic Russian regions
As we have shown in Table 1, some federal districts (Central, Northwestern, Southern,
Ural), regardless of having big ethnic groups too in addition to Russians, still do not go
below 50 percent when it comes to the number of Russian population in all federal sub-
jects included into those federal districts. Others (Volga, Siberian, Far Eastern, North Cau-
casus) include several federal subjects where Russian population is from 47 percent
(almost half of the total population of the region) to 1 percent (almost non-present). In
what follows, we will show cross-regional disparities between these two big groups of
Russian federal districts in relation to access and use of ICTs by the local population. We
realize that one of the limitations of this approach is the focus on a broader entity, i.e.
federal district, instead of the analysis of federal subjects where Russians are not the
major ethnic group. It would probably be indeed more logical to examine for instance
Table 3. Digital access. types of access included in the digital capital.
Access type Access operationalized
Digital equipment
Devices used to access the Internet (1) Mobile phone or smart phone
(2) Laptop
(3) Tablet
(4) Desktop computer
(5) Multi media or game player
(6) Smart TV
(7) Other devices (e.g. e-book reader, Smart
(8) (8) Other
Connectivity (lack of access)
In which of the following settings do you most frequently access the
(1) At home
(2) At my friendshome
(3) At school/university
(4) At work
(5) In a café
(6) In the library
(7) Free Wi-Fi anywhere
(8) (8) Other
Time spent online
How long have you been using the Internet? (1) Over 20 years
(2) (2) 1020 years
(3) (3) 510 years
(4) (4) 15 years
(5) Less than one year
(6) (6) Hard to say
Support/Formal Training
Request for help and formal training I have asked and oered help in the past 3 months
I had/didnt have formal training in using the
Source: adapted by Gladkova from Ragnedda and Ruiu (2020: 43).
Table 4. Digital competences. Competences included in the digital capital.
Competence area Competences operationalised
Competence area 1: Information and data literacy
Browsing, searching, ltering data,
information and digital content
I am condent in browsing, searching and ltering data, information and
digital content
Evaluating data, information and digital
I regularly verify the sources of the information I nd
I know when and which information I should and should not share online
Managing data, information and digital
I regularly use cloud information storage services or external hard drives to
save or store les or content
Competence area 2: Communication and collaboration
Interacting through digital technologies I actively use a wide range of communication tools (e-mail, chat, SMS,
instant messaging, blogs, micro-blogs, social networks) for online
Sharing through digital technologies I know when and which information I should and should not share online
Engaging in citizenship through digital
I actively participate in online spaces and use several online services (e.g.
public services, e-banking, online shopping)
Collaborating through digital technologies
Netiquette I have developed strategies to address cyber bullying and to identify
inappropriate behaviorsManaging digital identity
Competence area 3: Digital content creation
Developing digital content I can produce complex digital content in dierent formats (e.g. images,
audio les, text, tables)
Integrating and re-elaborating digital
I can apply advanced formatting functions of dierent tools (e.g. mail
merge, merging documents of dierent formats) to the content I or others
have produced
Copyright and licences I respect copyright and license rules and I know how to apply them to
digital information and content
Programming I am able to apply advanced settings to some software and programs
Competence area 4: Safety
Protecting devices I periodically check my privacy setting and update my security programs
(e.g. antivirus, rewall) on the device(s) that I use to access the Internet
Protecting personal data and privacy I use dierent passwords to access equipment, devices and digital services
Protecting health and well-being I am able to select safe and suitable digital media, which are ecient and
cost-eective in comparison to othersProtecting the environment
Problem solving
Solving technical problems I am able to solve a technical problem or decide what to do when
technology does not work
Identifying needs and technological
I can use digital technologies (devices, applications, software or services) to
solve (non-technical) problems
Creatively using digital technologies I am able to use varied media to express myself creatively (text, images,
audio and video)
Identifying digital competence gaps I frequently update my knowledge on the availability of digital tools
Source: Ragnedda and Ruiu (2020:4346).
Table 5. Factor loadings of the variables used for the Digital Access Index.
Digital access
Digital equipment .770
Support .433
Online experience .649
Connectivity .520
Note: KaiserMeyerOlkin (KMO) test = 553; Bartletts test, p< .000.
Table 6. Factor loadings of the variables used for the Digital Competence Index.
Digital Competences
Problem solving .791
Safety .815
Content creation .753
Note: KaiserMeyerOlkin (KMO) test = .662; Bartletts test, p< .000.
just Republic of Tyva (82 percent of Tyva vs. 16.3 percent Russians) instead of the whole
Siberian federal district, where this republic is located. However, since most data on Inter-
net access in the Russian regions, as well as digital literacy, Internet audience numbers and
other statistics are related to the federal districts and not federal subjects this would limit
our study substantially. Also, we would like to show in this paper broader cross-regional
dierences and comparisons contributing to ongoing academic discussion about digital
inequalities in Russia (Vartanova and Gladkova 2019; Gladkova, Garifullin, and Ragnedda
2019a; Volchenko 2016; Deviatko 2013), so a broader focus on Russian federal districts
should make sense here.
To start with, it should be noted that the digital divide of the rst level is still a serious
issue in Russia. General Internet penetration rate in Russia is 72 percent, and mobile Inter-
net penetration rate is 56 percent (GfK), which is of course very dierent from many other
countries of the world: e.g. UK with 94.7 percent, UAE with or 96.9 percent or North
America with 95 percent general Internet penetration rate (Internet World Stats). Recent
statistics shows that around 30 percent of the Russian population do not use Internet at
all (Figure 3). There are plenty of reasons for that, including diculties with making infra-
structure available in remote parts of the country, costs for building new infrastructure and
laying optic ber lines on territories with harsh climatic conditions and complicated relief,
dierent urbanization level of Russian regions, location of some territories, including for
instance the city of Norilsk, the northernmost city in Siberia, in the continuous permafrost
zone, digital literacy and motivation divides, etc. Although statistics does not specify how
many of those 30 percent of the population excluded from online space belong to non-
Table 7 . Component matrix of the combination between Digital Access Index and the Digital
Competence Index.
Digital Capital
Digital competences .729
Digital access .729
Note: KaiserMeyerOlkin (KMO) test = .500; Bartletts test p= .249.
Figure 3. Daily, weekly and monthly Internet audience numbers in Russia (% of the total population),
winter 2017/2018). Source: Internet v Rossii: 20172018. Our elaboration.
Russian ethnic groups, we believe they should constitute a considerably big part of it. This
is partially proved by previous research that showed that in many cases people lacking
access to the Internet live in remote areas, usually small cities and villages, where provid-
ing broadband or mobile connection to the Internet can be economically and technically
uneasy where in fact small ethnic groups often reside (Vartanova and Gladkova 2019).
What should be added here is that Russian federal districts dier from each other by
many factors, which can consequently result in digital inequalities between regions.
These include dierences by the size of economy that shows that Central federal district,
Ural federal district and Northwestern federal district are the leading ones in the country
(Reiting regionov Rossii po zarplatam 2017; Reiting sotsialno-ekonomicheskogo polozhe-
niya subjekrov RF po itogam 2017 goda). The same goes for the GDP per capita rates,
where Central (6,16,366 rubles) and Ural (758 885 rubles) federal districts again take the
leading position (Reiting sotsialno-ekonomicheskogo polozheniya subjekrov RF po
itogam 2017 goda). Central and Northwestern federal districts are also absolute leaders
in terms of urban population numbers (81.3 percent urban vs. 18.7 percent rural in the
Central federal district; 83.5 percent urban vs. 16.5 percent rural in the Northwestern
federal district), which can be probably explained by the proximity of these areas to the
two biggest megapolises in the country, overall economic and infrastructure development
of these federal districts, and other factors. North Caucasus (49.2 percent vs. 50.8 percent)
federal district for example has on the contrary bigger rural population numbers (Regiony
Rossii; Sootnoshenie gorodskogo i selskogo naseleniya po subjektam Rossiskoi Federatsii).
Keeping in mind these cross-regional disparities, let us now look at the Internet user
numbers in Russian federal districts (Figure 4). We can notice that federal districts
where Russians are the biggest ethnic group in most federal subjects (and which in fact
show better results by economic parameters and urbanization level mentioned in the pre-
vious paragraph, e.g. Central or Northwestern) are among the leaders by the number of
daily Internet users too, while others, where non-Russian ethnic groups are more numer-
ous (e.g. Volga) are lacking behind. This is not a universal trend though, since for example
Far Eastern federal district (with 49.9 ethnic Yakuts (Sakha) vs. 37.8 ethnic Russians in the
Republic of Sakha (Yakutia)) is one of the country leaders by the number of daily Internet
audience, having increased this number by a record 7 percent since 2016. The latter trend
Figure 4. Daily Internet users in Russian regions (% of the total Internet users in federal districts).
Source: Public Opinion Fund. Our elaboration.
can possibly be explained by the active implementation of the Program of Eliminating
Digital Inequality in Russia, which was launched in 2014 (Programma po ustraneniu tsifro-
vogo neravensta v Rossii). Although the program has all-Russia coverage, less developed in
technological sense regions have been receiving special attention and support in that
program. The set of activities within that program includes building kilometers of optic
ber cables, installing free WI-FI spots in settlements with over 250 inhabitants and
other projects. As an outcome of the program, Internet coverage of remote areas was
increased, and the number of Internet users grew too, contributing to overall positive
dynamics in the regions, including Far Eastern part of the county.
The number of Internet users is in many ways determined by the cost of connection.
Research showed that a cheaper cost of connection increases the probability of using
ICTs, thus reducing the rst level of digital divide (Engelbrecht 2008). In our case,
making Internet connection both xed and mobile aordable to the people living in
all federal districts of Russia and belonging to dierent ethnic groups is particularly impor-
tant, since it will foster their presence in online space, as well as overall equality and
inclusion. The average cost of xed Internet access in Russia is 404 rubles per month,
and the average cost of mobile Internet access is 281 rubles (Razvitie Interneta v regionah
Rossii). However, given unprecedented scale of the Russian Federation, remoteness of
regions from the federal center and, therefore, the more expensive backbone trac; dier-
ences in the transmission channels of Internet trac (in the Far East and Siberia the Inter-
net is provided mainly through more expensive satellite links); and the low level of
competition at regional markets(Nagirnaya 2015: 130), it is not surprising that the cost
of connection varies in dierent federal districts (Table 8).
By looking at this data, we do not observe any clear correlation between ethnic com-
position of the federal districts and cost of connection or Internet speed either. North Cau-
casus and Far Eastern federal districts, which are homes to Avars, Osetins, Kabardins,
Yakuts (Sakha), etc., have the highest costs of both xed and mobile Internet access in
the country, which can probably mean that for ethnic groups living in those regions it
is more challenging to go online (also because as we have shown earlier these two
federal districts are not the country leaders by the GDP per capita rates or the size of
economy). At the same time, Volga federal district, with high population of Tatars, Bashkirs,
Chuvash and Mari, has the cheapest Internet rates in the country, and shows good results
by the speed rates too. This means that again we cannot talk of one universal trend here.
What is clear though is that digital inequalities between Russian regions in general (e.g.
Table 8. Cost of xed and mobile Internet access in Russian regions (spring 2016).
Federal district
of Russia
Cost of unlimited xed Internet
access at speed over 3 Mbit/sec
(rubles per month)
Cost of mobile Internet access
with free trac provided
(rubles per month)
Amount of free
mobile trac
provided (GB)
Central 367 40 296 4.8
Northwestern 419 28 254 3.6
Ural 414 26 291 4.1
Siberian 427 27 290 3.7
Volga 365 29 241 4.8
North Caucasus 503 27 300 5.1
Southern 402 24 252 4.6
Far Eastern 624 19 454 3.8
Source: Yandex.
Central and Far Eastern), and between those where ethnic groups are most present again
compared to ethnic Russians living there (e.g. Volga vs. North Caucasus) are certainly on
Finally, let us try to look for correlation between ethnic composition of the Russian
federal districts and digital literacy index there. The survey conducted by ROCIT (Indeks
tsifrovoi gramotnosti) together with partner organizations since 2015 measures the
level of Russian usersdigital literacy. More specically, the digital literacy indexincludes
three main sub-indexes: digital consumption sub-index (broadband and mobile Internet
penetration rates, number of Internet users per region per capita, etc.), digital competence
sub-index (competence in searching for information online, using social networks, produ-
cing multimedia content for online, etc.), and digital safety sub-index (ability to protect
personal data, usersattitude towards illegal media content online etc.).
In 2018, the overall index of digital literacy in Russia reached 4.52 points out of 10, with
digital competence sub-index estimated at 5.44 points, digital consumption sub-index
4.49 points, and digital safety sub-index 3.29 points (Table 9). The study was conducted
in Russia only, therefore not allowing for cross-national comparisons at this point.
Overall, we can see a clear dierence between federal districts of Russia, where some
regions, including Northwestern (7.99 total points) and Far Eastern (7.32) are absolute
leaders, and others, including Volga (2.31) and North Caucasus (1.42) obviously lack
behind. This does not mean of course that all Internet users based in particular federal dis-
tricts are less qualied or less careful when it comes to fact-checking, following norms of
ethics, etc. compared to those based in other regions of the country, since within each
federal district there may be considerable dierences too, across republics, okrugs,
oblasts and other territorial formations. Still, if we discuss digital divide of the second
level here, approaching it from a digital literacy perspective, we will see that some
regions with multiple ethnic groups (e.g. Volga and North Caucasus) show worse results
compared to those where Russian population constitute a majority (e.g. Central and North-
western). Just like in the previous cases, this is not a universal trend, rst and foremost due to
good position of the Far East in the ranking (second place by digital literacy in the country).
Micro analysis: digital capital and ethnicity in Russia
To analyze the relationship between digital capital and ethnic group is Russia, we followed
the model proposed by Ragnedda and Ruiu (2020). Despite the fact that this model has
been earlier tested in another national context (Ragnedda, Ruiu, and Addeo 2019), this
is the rst attempt not only to try it out in Russia but also to shift focus on the ethnicity
Table 9. Digital literacy in Russian regions (2018).
Federal district of
Digital literacy
Digital consumption
Digital competence
Digital safety
Central 5.67 6.68 6.66 3.42
Northwestern 7.99 9.93 8.94 5.34
Ural 4.69 5.1 4.31 4.83
Siberian 4.14 3.38 5.15 3.47
Volga 2.31 3.13 3.19 0.37
North Caucasus 1.42 0.5 0.86 3.03
Southern 3.52 1.41 5.88 2.25
Far Eastern 7.32 5.56 7.06 9.29
Source: Regional Nongovernmental Centre for Internet Technologies.
factor in the analysis. Despite the sample is not representative and the results are not gen-
eralizable, the Digital Capital Survey shows some preliminary and indicative results that we
plan to elaborate in our future work, based on a larger representative sample.
We have rst, as mentioned in the methodssection, calculated the Digital Capital Index
and then we have tested against two dierent variables, namely ethnicity (Figure 5) and
place of living (Figure 6), by using simple bivariate analysis. Regardless of evident limits
of the bivariate analysis, the main idea here is to propose and discuss some preliminary
ndings and complement the macro analysis with some primary data that might shed
light onto how the individual level of digital capital interacts with place of living and eth-
nicity. These two variables are particularly interesting in Russia, given its variegate and
multiethnic environment and were therefore picked up purposefully.
In both cases, the results of the bivariate analysis seem to conrm and reinforce the
ndings obtained by using secondary data on a macro level. More specically, as we
can see from Figure 5, there is a slight dierence among Russian and non Russian users,
in terms of digital capital level. An important thing to mention here is that we attributed
to non Russianall users that claimed their belonging to other ethnic group when lling in
questionnaire (i.e. Yakuts, Tatars, Bashkirs and others), thus forming two large groups
based on ethnicity (Russians and non Russians) and not examining each smaller ethnic
group separately at this stage (e.g. comparing Yakuts with Tatars, etc.). However, each
of these two large groups is certainly not homogeneous: there are dierences between
Russians in terms of digital capital level, and also between and within particular ethnic
groups among non Russians. It would be therefore not correct to claim that Russians
have a higher level of digital capital than smaller ethnic groups in the country simply
based on their ethnic belonging. This is rst of all because our sample is not representative
and therefore reveals some indicative trends only, which still need to be conrmed further
on, and second because each ethnic group is a complex, heterogeneous community
where digital capital level can depend on many factors not directly related to ethnicity,
such as age, income and others (Ragnedda, Ruiu, and Addeo 2019).
Yet, despite the dierence we show in Figure 5 is not signicant, this data is in line with
the macro level results that show both a higher level of Internet penetration and digital
literacy in areas characterized by higher presence of citizens who belong to the Russian
group (e.g. Central and Northwestern federal districts). These two elements (Internet pen-
etration and digital literacy) are both included in the Digital Capital Index that, as men-
tioned above, summarize both digital access (external resources to access to the
Internet) and digital competences (internalized abilities to use the Internet).
Figure 5. Digital Capital and ethnicity. Source: our elaboration.
The above results are further reinforced if we look at the dierence in terms of place
where users live. Indeed, as we can see from Figure 6, those living in small villages tend
to have, on average, a lower level of digital capital, compared to those living in big
cities. The dierence is particularly signicant if we compare users living in small towns/
villages and those living in cities with population above 1 million. Evidently, this result
may be inuenced by many other variables that a simple bivariate analysis cannot
reveal (e.g. those living in big cities may have dierent jobs that require both more special-
ist digital skills and higher education, thus inuencing the individual level of digital capital)
and which require further analysis.
The use of both primary and secondary data in this study helped us understand digital inequal-
ities in Russia better and map out possible relations between dierent levels of the digital divide
on the one hand and ethnicity, as well as place of living of dierent ethnic groups on the other
hand. In this context, we did not manage to reveal any direct linkages between ethnic compo-
sition of the Russian federal districts and digital inequalities there (RQ1). We can see that some
Russian regions, which are home to ethnic groups that oftentimes outnumber Russians (Volga
federal district with Tatars and Chuvash; or North Caucasus with Chechens, Ingush and others)
show somewhat worse results in terms of digital literacy and either speed/cost of connection or
daily Internet audience numbers compared to federal districts with more monolithicethnic
composition (Central and Northwestern). This means that ethnic groups living in those
regions can experience diculties with going online, communicating in native languages,
and of course using benets of being online for professional and personal reasons, thus
being subject to the third level of the digital divide (Ragnedda 2018).Atthesametime,this
is not a general trend, since as we have shown Far Eastern federal district, which has a big popu-
lation of Yakuts (Sakha) and Chukchi, is one of the country leaders by Internet audience
numbers and digital literacy, lacking behind by cost and speed of connection though.
Federal districts are of course not homogeneous either: even within Far Eastern federal
district there are clear dierences between Chukotka authonomous okrug, where people
still use satellite Internet connection (quite low-speed and expensive) due to lack of
alternative options, and the city of Vladivostok, one of the most technologically developed
cities in the region. That is why it is hard to say that ethnic groups living in a particular
Figure 6. Digital Capital and place of living. Source: our elaboration.
federal district are most advantageous than others, since within each federal district and
each ethnic group the use of Internet may be signicantly related to other personal and
objective factors (age, income, education, motivation, availability of ICTs in a particular
location, etc.) (RQ 2). An important role here also belongs to the regional / local policy
aimed at providing digital access to people living in that region, both in terms of
making ICTs available to population and in terms of digital literacy development.
Results of the digital capital study, despite being indicative, show that those belonging
to the main ethnic group in a country (in our case Russians) and those living in big cities
tend to have a higher level of digital capital (RQ3). Given the fact that digital and social
inequalities tend to reinforce each other (Ragnedda 2017), those who are more socially
advantaged tend to get the most out of the Internet, further reinforcing their social pos-
ition by using ICTs. However, as we already noted, ethnicity should not be treated as an
independent variable: there are plenty of other factors including education, social
status, age, economic state of the region, etc. that may inuence the result. Our paper
explores the importance of ethnicity as a factor determining the level of the digital
capital but does it from a Cultural Discourse Studies perspective, as part of communi-
cation/discourse research on local cultural context (Shi-xu 2016: 4).
Furthermore, in case of multiethnic and multicultural Russian setting, Internet and ICTs
play an important role in building democratic society, where diverse ethnic, religious, cul-
tural, linguistic groups enjoy equal freedom of expression and access to information,
possess digital skills and media literacy, and are able to use this freedom in both oine
and online communication with a high level of received diversity. This type of diversity
includes, according to Peruško (2013: 207) the possibility of access to a diverse mix of
media and media programs that can (or should) contribute to media literate active citi-
zens. We argue that in a situation when any group of people is excluded from cyberspace
due to lack of equipment, weak access to the Internet or lack of competences and media
literacy to use ICTs, one can hardly speak of a proportional representation of dierent
views in cyberspace and subsequently of received diversity. Furthermore, such
groups are excluded from access to information that will give them sucient basis for
making enlightened judgments, possibility to participate in collective decision-making,
which are characteristics of a democratic society. Moreover, safeguarding equal access
and representation of ethnic groups in online space contributes to saving cultural-intellec-
tual identities (Shi-xu 2009: 41) of people, which is particularly important in case of multi-
ethnic and multicultural societies such as Russian society.
Lastly, we would like to underline that digital capital index, capturing both the quality
and types of access and the abilities and competences of using the Internet, is useful to
provide a specic picture of the capacity to access, use and obtain benets from the
ICTs among dierent social and ethnic groups in the society. This is what Ragnedda &
Ruiu (2020)dene as the double-loop processwhereby those with higher level of
digital capital (in our case Russians living in big cities) can more likely to invest and transfer
oine resources online and at the same time cumulate digital capital to increase their
social position and social status. For this reason, for both scholars and policy makers
involved in tackling digital inequalities, it is important to monitor if any dierences in
the level of digital capital occur among dierent ethnic groups or between people
living in dierent part of the country and cities. This is particularly true in Russia that, as
we have seen, is characterized by the presence of multiple ethnic groups spread across
numerous territorial entities with dierent socioeconomic state and urbanization level. We
expect therefore to continue this study by increasing sample and making it representative
to see if the indicative results we have obtained so far can be further conrmed and devel-
oped on a broader national scale.
1. An important thing to understand hereinafter is that ethnic belonging of users is dened by
the people themselves (i.e. the ethnic group they identify themselves with something all-
Russia census is aimed at nding out) and not to their nationality, since most ethnic groups
living in Russia (Tatars, Bashkirs, Yakuts, etc.) have Russian citizenship.
4. In Table 1, we list all Russian federal subjects (out of 85 in total) where total population of Rus-
sians is less than 90 percent according to the most recent data of 2010.
Notes on contributors
Dr. Anna Gladkova is Leading Researcher and Director of International Aairs Oce at the Faculty of
Journalism, Lomonosov Moscow State University (Russia). She is also IAMCR Ambassador in Russia
and vice-chair of the Digital Divide Working Group in IAMCR.
Professor Dr. Elena Vartanova is Dean and Chair in Media Theory and Economics at the Faculty of
Journalism, Lomonosov Moscow State University (Russia). She is the President of the National Associ-
ation of Russian Mass Media Researchers and corresponding member of the Russian Academy of
Dr. Massimo Ragnedda is Senior Lecturer in Mass Communication at Northumbria University, New-
castle (UK). He is vice-chair of the Digital Divide Working Group in IAMCR and co-convenors of NINSO
(Northumbria Internet and Society Research Group).
Disclosure statement
No potential conict of interest was reported by the author(s).
This work was supported by Presidential grant council for the state support of young Russian scho-
lars: [Grant Number MK-795.2020.6].
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... Another limitation is that the study was conducted in an online format. Because of the 'digital divide' observed in Russia (Gladkova et al., 2020), not all groups of citizens have equal access to the Internet, which may lead to the underrepresentation of certain social groups on crowdsourcing platforms. However, since we did not set out to obtain a sample that is representative in the strict sociological sense, the influence of this factor should not be considered determinative. ...
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... Only with the help of digital competencies, the mix of ICTs in education can have an impact, both in terms of digital literacy and in terms of academic performance. Gladkova et al. (2020) study states that ethnicity plays a major role in defining the digital capital. People belonging to the ethnic majority of the respective country and those living in metropolitan or cosmopolitan cities tend to have a higher level of digital capital. ...
We conducted a randomized controlled experiment to investigate the role of descriptive captions (positively and negatively worded) and ideological beliefs (Right Wing Authoritarianism and Social Dominance Orientation) on viewers’ evaluations of two popular British Royal family members namely Meghan Markle and Kate Middleton. Participants included 300 undergraduate students from Dunedin, New Zealand who were randomly assigned to one of the four conditions: (1) Pro-Kate, (2) Anti-Kate, (3) Pro-Meghan or (4) AntiMeghan captions accompanying the parallel images of these Royal members. We also included several distractor variables about other Royal family members and traditions. Outcomes were recorded as evaluations of six royal members (Charles, Diana, William, Harry, Kate, and Meghan). We found no significant effect of caption manipulation on outcome evaluations of Meghan and Kate. However, social dominance negatively correlated with Meghan and Harry whereas authoritarianism positively correlated with ratings of Charles. Our results indicate that a one-off exposure to biased media regarding celebrities may not significantly alter audience’s evaluations of them, but ideological beliefs may influence this process, nonetheless.
... Only with the help of digital competencies, the mix of ICTs in education can have an impact, both in terms of digital literacy and in terms of academic performance. Gladkova et al. (2020) study states that ethnicity plays a major role in defining the digital capital. People belonging to the ethnic majority of the respective country and those living in metropolitan or cosmopolitan cities tend to have a higher level of digital capital. ...
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It is widely assumed among academicians that the COVID-19 pandemic has negative implications for the education of school students. However, institutions tried to balance that limitation by using online education, and there exist some inequalities among students. Most of the studies conducted during COVID-19 on online education focused on urban school students and their access to online education. In particular, rural school students and their online education remain an open question. Twenty in-depth interviews with rural student respondents determine the fundamental problems and challenge the rural school students’ face in online education during the COVID-19 pandemic lockdown. The study identifies six major problems of rural students: inadequate technology, unacquainted academic atmosphere, digital disconnect, physical well-being, the distractions inherent with the medium, and digital illiteracy. The identified constraints draw inferences to a critical concept in online learning that is digital inequality. Digital inequality refers to the disparity in the access, distribution of technology, information because of various socio-economic and cultural factors. The study also discusses the suggestions of rural students regarding the betterment of online education. The recommendations from the rural students include providing appropriate technological infrastructure, facilitating technical assistance and providing a convenient academic atmosphere. The suggestions are pointing towards the idea of digital inclusion that is vital in online education. Digital inclusion is defined as the ability of individuals or groups of people to access and use information and communication technologies. It is not only about access in a broader sense the opportunities of using innovative hardware and software technology, content and services, getting proper digital literacy pieces of training and the effective use of these services. The findings of the study will help to bridge the disparities in online education. These findings will help the academic community to identify the needs of rural children. It will help build infrastructure for online learning and give extensive support to the school children of rural communities. These findings are also vital for the communication scholars as the disparity in the distribution of information and knowledge is a prime concern for them.
... Ragnedda, Senior Lecturer in Mass Communication at the University of Northumbria, Newcastle (UK), determined that people who belong to the ethnic majority and live in large cities tend to have higher levels of digital capital (Gladkova et al., 2020). ...
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The paper discusses the digital inclusion of major and minor ethnic groups in Russia by comparing three broad categories of digital resources, identified in this study as components of the index of inclusion: information and communications technology (ICT) access, skills, and extent of engagement with technologies. Based on these components/subindices, we constructed an index of digital inclusion for the Russian context and tested it on a representative national sample of 765 Internet users (596 Russians, 196 Yakuts). Our study showed that Russians use more platforms for online access (mobile phones, laptops, consoles, smart TV, etc.) than Yakuts and access the Internet through a bigger number of locations, not being limited to home and/or office only. They also have higher level of social, technical, and creative ICT skills, and demonstrate higher levels of digital engagement and overall digital inclusion. We argue that the explanation here lies first of all in the geographical domain, that is traditional location of ethnic minority (Yakuts) in a region that is less digitally advantaged in terms of Internet access, cost, speed, and other factors (Far Eastern federal district), and is not related to ethnicity itself. We think therefore that this study is a good illustration of how the first and the second levels of the digital divide interrelate and influence each other, leading to a situation when people with lower access to the Internet and ICTs have lower skills and competences to use them, therefore risking to become digitally and also socially excluded.
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The COVID-19 pandemic affected education worldwide, and journalism faculties and students were not exceptions. In the Russian Federation, all educational institutions, including journalism schools, were obliged with 1 day’s notice to switch their classes from regular face-to-face teaching to remote and online formats. The abruptness of this transfer caused a variety of reactions in academic and student communities. This article presents a country-oriented study of how the pandemic affected Russian journalism education. Executives of 15 Russian journalism schools in a geographical spread across the nation evaluate how their faculties and students coped with the classroom shutdowns and discuss both the stressful and motivating practices they have experienced. In brief, they could be described in three typologies: digital, methodological, and communicational. The study uses educational perspectives that could be exercised in the development or renovation of journalism education practices.
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Introduction . The sociodynamics of students’ digital capital in the context of an epidemiological crisis has acquired special characteristics. The speed, focus and technologies for the formation of digital capital have changed significantly and are expressed in new content features and characteristi cs of sociodynamics. Methodology and sources . An integrative approach to the study and analysis of the digital capital sociodynamics is used, which consists a set of theoretical and methodological positions in the study of digital capital proposed by resea rchers earlier. An approach to the sociological interpretation of the concept of digital capital, which is characterized by systemic and integrative characteristics, is presented. The formed theoretical and methodological platform served as the basis for c onstructing an empirical research methodology. Results and discussion . Methodological approaches to the digital capital definition as an object of research are generalized. The contradictions and fragmentation in the interpretation of digital capital in private research and the relevance of sociological understanding of the essence of digital capital are shown. The tendencies and trends of the sociodynamics of digital capital during the pandemic have been empirically confirmed. Conclusion . The article presents some trends in the sociodynamics of students’ digital capital in a pandemic: first, the stimulating role of the pandemic in the development of digital competencies; secondly, changes in the direction of mastering digital competencies; third, analysis of the activity and intensity of changes in digital competencies; fourth, the intensification of the development of digital technologies related to the social aspects of interaction in the context of a lockdown.
One of the characteristics of the post-literacy era is the emergence of the communication gap between “analogue” and “digital” media generations. Among their distinctive features are not only different media practices, often in mismatched media environments, but also various thematic orientations of generational media communications. Based on the author’s socio-cultural concept of media generations and the use of the Sketch Engine, a modern cloud tool for studying large text collections, thematic generational dominants in the media were identified and the perspectives for intergenerational media communication are formulated.
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Starting from the assumption that digital capital is a capital in its own right, and can be quantified and measured as such, the authors of this book examine how digital capital can be defined, measured and impact policy. Using the Bourdieusian lens, this book makes a critical contribution to the field by examining in depth the notion of digital capital and by introducing a new theoretical toolkit in order to fully conceptualise it. Against this theoretical background, the authors propose a set of indicators that can be used to measure digital capital at an individual level. Ultimately, readers will learn how this can be used by policy makers to tackle social inequalities which are based on the digital exclusion of citizens.
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This article develops a Digital Capital Index by adopting the definition provided by Ragnedda, who defines Digital Capital as the accumulation of digital competencies and digital technologies, and the model for measuring it developed by Ragnedda and Ruiu. It aims to develop a measure that can be replicated for comparison in different contexts. This article contributes both theoretically and empirically to the literature by (a) consolidating the concept of Digital Capital as a specific capital and (b) empirically measuring it. A Digital Capital Index is developed through an exploratory factor analysis (EFA) and validated with a representative sample survey of 868 UK citizens. The validation procedure shows that the Digital Capital Index is associated with socioeconomic and sociodemographic patterns, such as age, income, educational level and place of residence, while it appears not to be related to gender.
This article contains research about the phenomenon of digital inequality among different social groups in Russia. The short literature review, conducted in it, concerns the factors, which have an impact on the rise and spread of digital inequality. The types of digital inequality have been analyzed, and each of them has the relative digital divide indicators calculated. It has been revealed, that the digital divide in terms of the Internet and information and communications technology access between the rural and urban population has been decreased, however the divide in terms of digital skills has been increased. There is almost no digital divide in ICT among the middle-aged groups, and it is constantly decreasing. Although the digital divide among the older-aged groups is also growing down, it is still high, and the divide in digital skills remains unchanged. It is noted, that the country’s territory in length objectively increases the IT infrastructure costs, hampers providing for the high-quality internet-connection for the rural people and is the reason for digital divide in terms of Internet-access.
The paper analyzes the current state of ethnic media outlets (print, broadcasting and online) in Russia, i.e. media produced and disseminated in the three national republics of Russia (Tatarstan, Bashkortostan and Chuvashia) and in languages of the biggest ethnic groups living in those areas (the Tatar, the Bashkir and the Chuvash), and discusses their possible trends of development. Using open data analysis, we look into funding options (the proportion of media with state budget only and those with state budget and alternative sources of financing, such as advertising revenues, subscription, donations, sponsorship, etc.) and ownership (the proportion of state institutions and private companies, organizations, individuals, etc. as media owners). At the end, we consider whether ethnic media in Russia today follow the traditional state model (i.e. are primarily state-owned and state-funded) or are gradually shifting towards an ‘alternative’ (i.e. non-state) one in terms of financing, ownership, management and other factors.