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If the global brain is a suitable model of the future information society, then one future of research in this global brain will be in its past, which is its distributed memory. In this paper, we draw on Francis Heylighen, Marta Lenartowicz, and Niklas Luhmann to show that future research in this global brain will have to reclaim classical theories of social differentiation in general and theories of functional differentiation in particular to develop higher resolution images of this brain’s function and sub-functions. This claim is corroborated by a brain wave measurement of a considerable section of the global brain. We used the Google Ngram Viewer, an online graphing tool which charts annual counts of words or sentences as found in the largest available corpus of digitalized books, to analyse word frequency time-series plots of key concepts of social differentiation in the English as well as in the Spanish, French, German, Russian, and Italian sub-corpora between 1800 and 2000. The results of this socioencephalography suggest that the global brain’s memory recalls distinct and not yet fully conscious biases to particular sub-functions, which are furthermore not in line with popular trend statement and self-descriptions of modern societies. We speculate that an increasingly intelligent global brain will start to critically reflect upon these biases and learn how to anticipate or even design its own desired futures.
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Futures of a distributed memory. A global brain wave measurement (1800-2000)
Accepted for publication in Technological Forecasting and Social Change
Steffen Roth, La Rochelle Business School, France*
Carlton Clark, University of Wisconsin-La Crosse, USA
Nikolay Trofimov, Russian Academy of Science, Russia
Artur Mkrtchyan, Yerevan State University, Armenia
Markus Heidingsfelder, Habib University, Pakistan
Laura Appignanesi, University of Macerata, Italy
Miguel Pérez-Valls, University of Alméria, Spain
Jan Berkel, Independent, Portugal
Jari Kaivo-oja, Turku School of Economics, Finland
*Corresponding author: roths@esc-larochelle.fr
Abstract If the global brain is a suitable model of the future information society, then one
future of research in this global brain will be in its past, which is its distributed memory. In
this paper, we draw on Francis Heylighen, Marta Lenartowicz, and Niklas Luhmann to show
that future research in this global brain will have to reclaim classical theories of social
differentiation in general and theories of functional differentiation in particular to develop
higher resolution images of this brain’s function and sub-functions. This claim is corroborated
by a brain wave measurement of a considerable section of the global brain. We used the
Google Ngram Viewer, an online graphing tool which charts annual counts of words or
sentences as found in the largest available corpus of digitalized books, to analyse word
frequency time-series plots of key concepts of social differentiation in the English as well as
in the Spanish, French, German, Russian, and Italian sub-corpora between 1800 and 2000.
The results of this socioencephalography suggest that the global brain’s memory recalls
distinct and not yet fully conscious biases to particular sub-functions, which are furthermore
not in line with popular trend statements and self-descriptions of modern societies. We
speculate that an increasingly intelligent global brain will start to critically reflect upon these
biases and learn how to anticipate or even design its own desired futures.
Keywords Global brain; Google Ngram Viewer; culturomics; secularization; capitalism;
functional differentiation.
1 Introduction
As researchers in technological and social change, we want to track and trace significant
trends in past and future societies. One such trend is secularization, the declining importance
of religion, which is so important to the self-concept of modern societies that the mere
thought of a trend reversal brings back memories of the Middle Age. Another widely
recognized trend is the growing influence or even dominance of the economy in our societies
today. There is also discussion on further and sometimes competing trends, which include the
prominent idea of an information society dominated by the mass media system. Yet another
stable trend is that these and similar trends have been assumed and implied rather than studied
so far, which constitutes a third order risk (Godet 1986) whenever we extrapolate the trend
truisms into the future, thus using the right tools to meet the wrong expectations. Most of us
nonetheless rely on traditional trend knowledge, while only a few have called or tried for
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systematic large-scale tests (Blumler and Kavanagh 1999; Kjaer 2010; Roth 2014; Roth et al.
2016), and our uncritical attitude to the facticity of some of the most significant trends in
modern societies is justified to the extent that their examination presents a veritable challenge
even in the plain middle of the presumed information age. The on-going proliferation of
information and communication technology in general and the Internet in particular has
indeed given hope that the analysis of social macro trends will be more feasible or at least
more convenient, but has also shown that a network of IT-supported interactions presents
more than a comprehensive search tool for big data. As much as any complex tool, the
Internet is observed to have taken on a life of its own, which in the case of the World Wide
Web encompasses an entire globe. Pioneers go as far as to state that this “single information
processing system (…) plays the role of a nervous system for the planet earth”, thus referring
to the Internet as global brain (Heylighen and Lenartowicz 2016).
In this article, we use a considerable and quite representative proportion of the Internet to
review macro trend hypotheses such as the secularization, economization, mediatization, or
politicization of society. We draw on the global brain paradigm, first, as a constant reminder
that the Internet is not one of our usual research tools, and, second, to further develop the
paradigm by contributing a method we refer to as global brain wave measurement. Somewhat
similar to the pending planetary electroencephalography suggested by Russell (1982), our
procedure will measure certain aspects of the electromagnetic activity of the global brain. Yet,
the comparably short history of the Internet also suggests that a traditional real-time
electroencephalography (EEG) will not be adequate to monitor long-term social macro trends.
It is due to the Google Books initiative, which has generated “the largest online body of
human knowledge”1 in the form of a word corpus of more than 25 million digitalized books,
that we see that the global brain has a memory older than the Internet itself, and that we still
can access this virtually pre-conscious memory using the Internet in an unprecedented way.
We hence used the Google Ngram Viewer, an online graphing tool that charts annual word
counts as found in the Google Book corpus, to run comparative analyses of word frequency
time-series plots for the English, Spanish, Russian, French, German, and Italian language
areas. The outcomes of this procedure positively resemble classical EEG recordings and
indicate that the attention the global brain devoted to religion, economy, politics, the mass
media and further social systems featured substantial changes in time and significant regional
differences. The results also suggest that a number of popular trend statements and definitions
of modern society are completely divorced from the global brain’s memories between 1800
and 2000.
2 Global Brain Waves: From electrophysiological to electrosociological brain wave
measurement
In our research, we used a small Internet tool to observe a big Internet database. Or put briefly,
we used the Internet to monitor the Internet. This situation is different from the case of a
traditional electrophysiological brain wave measurement, where the research in brains is
thought to be performed from a standpoint external to the examined brains. By contrast, our
research was literally in the global brain throughout the entire process. Clearly, we cannot
observe the global brain from the outside. Our only logical starting point hence was a
thorough exploration of our own research environment.
One of the most up-to-date, compact, and still comprehensive accounts of this research
environment has recently been published in Technological Forecasting and Social Change. In
their editorial to the special issue devoted to the global brain, Francis Heylighen and Marta
1 Only the bold beauty of this fittingly anthropomorphical metaphor made us quote the Wikipedia article on
“Google Books” as accessed on July 28, 2016.
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Lenartowicz (2016) introduce the concept as a realistic model of the information society.
They define the global brain “as the self-organizing, adaptive network formed by all people
on this planet together with the information and communication technologies that connect
them into a coherent system”. Their idea is clearly that ICT-mediated interactions have
increased interpersonal dependences up to the point where we can observe the emergence of a
single superorganism, “i.e. an organism (global society) consisting of organisms (individual
people)”, with the Internet playing the role of the nervous system for this planetary
superorganism. Next to the rapidly intensifying interdependences, the authors also stress the
constantly increasing information storage and processing capacities that go along with the
present Internet revolution. The authors conclude that we shall soon live to see a qualitative
leap in or to the evolution of an adaptive, globally distributed intelligence that has a life of its
own.
Among the many compelling contributions to the corresponding special issue we found co-
guest editor Marta Lenartowicz’ (2016) single-authored article particularly instructive as it
deviates from a number of classical assumptions in the global brain literature and even in her
above co-authored introduction. In “Creatures of the semiosphere. A problematic third party
in the ‘humans plus technology’ cognitive architecture of the future global superintelligence”,
where she argues that neither human beings nor IT-supported networks of human beings, but
social systems can be conceived as “the most advanced intelligence currently operating on
Earth” (Lenartowicz 2016). As she draws on the work of Niklas Luhmann (Luhmann 1995;
Luhmann 2012; Luhmann 2013), she defines social systems as autopoietic systems of
communication, the first emergence of which she traces back to the origins of spoken
language tens of thousands years ago. This approach is remarkable in two ways: First, she
proposes to change the traditional human-technology focus prevailing in the global brain
literature2 for a technology-communication focus, which, to our mind, is more suitable for the
observation of complex information and communication technology systems. This proposed
observational shift from networks of humans to networks of communications3 allows access
to a so far under-researched macro region of the global brain. Second, her short and
appropriate recourse to the history of communication and communication media suggests that
distributed intelligence might be older than the global consciousness about it (Heylighen
2011).
If we trace these two ideas back to their systems theoretical origins, then we find indeed that
the idea of a social global brain consisting of a network of communication and technology is
as plausible as is the classical idea of a bio-technological global brain made of human
organisms and technology. This is true particularly because a basic form of intelligence,
memory, is inherent to all forms (Luhmann 1997, p. 364), including all forms of
communication (Luhmann and Rasch 2002, p. 160). Communication as threefold selection of
information, utterance, and understanding operates in time, which implies the management of
the difference between past and future, the token of which is memory (Luhmann 2012, p.
350); and systems of communication imply memory in order to link one communication to
2 Theories that focus on human-technology linkages, or “humans-plus-technology,” and theorize the global brain
as a network connecting human beings are useful but still anthropocentric. Two important texts on network
society are Harrison White’s Identity and Control: A Structural Theory of Social Action (1992) and Manual
Castells’ The Rise of Networked Society (1996). More recently, in Networks of Outrage and Hope: Social
Movements in the Internet Age, Castells (2012) takes up the subject of networked social movements with
reference to the Arab Spring and other movements. We are more interested in autonomous social systems than in
networks of human beings.
3 For an extensive case made for a similar turn in organization studies including instructive visualizations see
(see also, e.g., Lenartowicz 2016, p. p. 178; Luhmann 2012).
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another. Memory is hence not an isolated subfunction of a social system, but rather involved
in all of its operations, and Luhmann emphasizes that “these operations are communications,
and thus not neurobiological changes in the state of the [biological] brain nor what enters the
awareness of a single consciousness” (id, p. 349). The more complex the social system, the
more complex its memory. We consequently can image highly complex forms of collective,
distributed, or simply social memory that are made of communication and nothing but
communication. The main function of all these forms of memory would be the same as with
all forms of memory: forgetting. This only prima facie counter-intuitive take on the memory
function is stringent insofar as the memorization of no matter what presents a necessarily
selective operation which recalls only very little information, thus filtering out numerous
alternatives.
Memory works as a filter located at the interface of the past and the future, and therefore
necessarily always in the present. As a filter, the
“function of memory relates to distinctions; or, more exactly, to indications of something as opposed to
something else. The memory operates with what has been successfully indicated and tends to forget the other
side of the distinction. Although it can also mark distinctions as forms, for instance, the distinction between good
and evil, it tends to forget what this distinction was distinguished from. The particularity of discrimination in the
forgetting/remembering schema is determined not least by language and is insofar a characteristic of social
systems. (Luhmann 2012, p. 351)
As every social system requires such a social memory, every society is, in its temporal
dimension, defined by the form of this filter. The key question, then, is which distinctions a
given society draws in which medium to manage its own history, and the token for the
particular way a given society executes this filter function is culture (Luhmann 2012, p. 355).
Archaic societies already had culture, i.e., a social memory concerned with the sorting of
more or less tangible objects and features in the medium of oral language. Yet, Luhmann
states that it was not until the Age of Enlightenment that cultures started to distinguish
between culture and nature as much as between different cultures, assuming that this reflexive
turn presented a necessary reorganization to align the social memory with the requirements of
an increasingly complex and dynamic modern society. Today, the reflexive memory of (post-)
modern societies is increasingly flexible and skilled in the use of distinctions, including those
that were constitutive for earlier forms of memory. As it is our ambition to analyze social
mega trends between 1800 and 2000, we shall be particularly interested in exploring this
stock of distinctions available for the organization (and constant re-organization) of a modern
social memory. This implies that we need to be concerned with social differentiation.
To date, we may distinguish four basic forms of social differentiation (see Table 1):
Equal
+
+
Segmentation
(Families, tribes, nations, etc.)
Centralization
(Civilizations, empires, etc.)
Functional Differentiation
(Economy, Science, Art, etc.)
Stratification
(Castes, estates, classes, etc.)
Table 1: Social Differentiation [Source: Roth (2015, p. 113)]
These basic forms of social differentiation may be used to tell a short history of human
society (Luhmann 1977; Luhmann 1990; Luhmann 2013). Segmentation was the dominant
form of social differentiation in archaic, oral societies, which were made up of both similar
and equal segments (see the top left quadrant). Yet, in the course of the Neolithic revolution,
processes of centralization occurred that turned some segments into centers and others into
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periphery (top right quadrant). Although centrality does not always constitute an advantage,
centralization of resources, influence, or attention often resulted in stratification, i.e. a process
by which subsystems of society are ranked into a hierarchy of dissimilar and unequal
subsystems (bottom right quadrant). In the transition to modern societies, however,
stratification was replaced by functional differentiation as the primary form of social
differentiation. Functional differentiation is defined as the distinction of dissimilar and equal
function systems (bottom left quadrant) such as the political system, economy, science, art,
religion, legal system, sport, health, education, and mass media system.4
It is important to note that older forms of social differentiation are not replaced but only
overruled by newer ones. Thus, we still observe segmentation of families residing in private
homes; however, abused or neglected children and battered spouses are now afforded
protection by the legal system, and children are subject to compulsory education. Social class
inequities are also still observable by the social sciences, and organizations (e.g., corporations,
universities, governments, militaries, bureaucracies) still have hierarchical structures, but
people are no longer born into fixed, unchangeable social strata with unequal legal rights. If a
significant percentage of a society remains poor, we tend to blame the education system or
call for reform of the economy, politics, or the healthcare system. That is to say, we don’t take
social inequality as a natural given. To take another example, universities and other
institutions are ranked, but these rankings are changeable. We also observe centers of power
(e.g., governmental or financial centers) with weaker peripheries (e.g., rural areas, rust belts,
Parisian suburbs). However, the key point is that the functional differentiation of the economy,
politics, law, education, healthcare, mass media, science, art, religion, etc., overrules older
forms of segmentation, stratification, and center/periphery organization. In our context, this
means that in modern societies all four basic forms of social differentiation are in principle
available to organize the modern social memory, although modern culture may be expected to
feature a certain bias to the principle of functional differentiation if it comes to the filtering or
realization of relevant information.
Functional differentiation obviously is the form of social differentiation on which we need to
focus in the context of our electrosociological global brain wave measurement, because the
observation of trends such as secularization or economization refers to changes in the
prominence of function systems such as religion or economy, and therefore implies functional
differentiation.
Our planetary EEG hence is sociological because we analyzed recordings of global brain
activities that indicate cultural fluctuations, i.e. changes in the relevance that specific forms of
social differentiation have for the self-organization of the social memory; and it is electro
because these culturomic recordings are produced by an Internet tool, as we shall demonstrate
in the subsequent section of this article.
3 Global Brain waive measurement: An operationalization using the Google Ngram
Viewer
3.1 The Google Ngram Viewer as socioencephalograph
In the previous section, we supported and radicalized Marta Lenartowicz’ (2016) work on
semiotic forms of superintelligence and exchanged the traditional biotechnological definition
for a sociotechnological definition of the global brain as the global system of communication,
including information and communication technology. We also drew on Niklas Luhmann to
demonstrate that, as much as any social system, this global social system features memory,
which is critical as the purpose of our brain wave measurement was to verify social mega
4 See Roth (2015) and Roth and Schütz (2015) for a more detailed account of the process of social differentiation
and a discussion on the current number of function systems.
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trends and hence required some form of access to the memories of the global brain. We also
explained why the key indicators of our research are necessarily related to the concept of
functional differentiation, the key principle behind the distinction between function systems
such as religion, economy, politics, legal system, science, education, or the mass media
system.
Our basic idea was to use the Internet to analyze how relevant the individual function systems
have been to the global brain within the last two centuries. This approach is adequate since
the ICT revolution in general and the Internet in particular considerably leveraged the
cognitive capacity of the global brain. Yet, it is also problematic because the Internet is
younger than the trends we intended to verify. We had therefore been lucky that the Internet
represents only one specific form of social memory next to older forms such as oral tradition,
writing, or printing (Lenartowicz 2016; Lenartowicz et al. 2016; Luhmann 2012, p. 178), and
we had been even luckier that the Google Books project operates at the interface of two of
these forms of social memory.
Officially announced in 2004, the Google Books project has scanned and digitalized over 25
million of the estimated 130 million published titles worldwide. The research potential of this
project was first recognized by a Harvard research team (Michel et al. 2011) in 2007. The
team performed quality checks, created a first consolidated Google Book corpus of more than
5 million books, coined the term culturomics for the “the application of high-throughput data
collection and analysis to the study of human culture” (ibid, 181), and developed a prototype
of what would finally become the Google Ngram Viewer, an online search tool that plots line
charts of annual word5 counts as found in the Google Book corpus. Today, the updated
version of the Ngram Viewer scans a corpus of over 8 million books containing hundreds of
billions of words in English, Spanish, Russian, French, German, Chinese, Hebrew, and now
also the Italian language [see Lin et al. (2012, p. 170) for an overview of the number of
volumes and ngrams for each language area]. The tool has been quickly discovered by
pioneers in the digital humanities and been used predominantly to analyze issues of language,
literature, history, and culture (Gibbs and Cohen 2011; Johnson 2010; Michel et al. 2011;
Nicholson 2012; Ophir 2010; Sparavigna and Marazzato 2015). There have also been first
attempts to establish culturomics research in the social sciences, e.g., in the context of a
retroactive forecasting of social movements like the Arab Spring (Leetaru 2011) or popularity
checks of sociological theories, scholars, fields, and methodologies (Chen and Yan 2016).
Using the Google Ngram Viewer means analyzing a corpus of words as found in books that
made their way to the Internet. Whereas the appearance of a word in a book is a matter of its
word importance, the appearance of a book in the Google Book corpus is a matter of book
representativeness. Although the designers of the Google Book corpora did their best to avoid
selection biases, the corpora have been criticized for containing words from exactly one of
each book, which favors merely prolific authors over possibly less prolific but more
influential authors (Pechenick et al. 2015). While the latter issue can only be addressed by
including – ultimately contingent – popularity indicators in the already giant dataset, the
former issue is interesting because it raises performativity issues that are important in any
research using interactive media. Again, we see that our research in the global brain literally
takes place in this global brain, which is true as the Google Books project continues and the
results of our research might enter the very memory region we screened. Our research is
therefore not likely to eventually co-perform the analyzed social mega trends, which presents
a methodological challenge as much as a paradoxical access cue for those who are interested
5 The basic units of the corpus are not words but n-grams, sequences of n1 letters, figures, or signs, including
misspellings and apparently meaningless expressions; thus the name Google Ngram Viewer. We shall
nonetheless use the term word for the sake of readability.
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in these trends “to anticipate them and to direct them towards the most desirable outcomes”
(Heylighen and Lenartowicz 2016).
In our research, we considered the words to be forms of communication in a communicative
medium (written language) and translated into another communicative medium (computer
language). We further assumed that the frequency with which these forms appear in the
respective medium as indicated by the Google Ngram plots be an appropriate approximation
to their importance; in fact, word frequency is deemed the “simplest and most impartial gauge
of word importance” (Kloumann et al. 2012, p. 1) or the popularity of objects, concepts, or
persons (Bohannon 2011; Ophir 2010). Moreover, our research builds on earlier applications
of the Google Ngram Viewer to social mega trend verification (Roth 2014; Roth et al. 2016),
which we complement and further develop in the following three dimensions: first, our
reference to the global brain concept makes our approach more intuitive, concrete, and
literally more reflexive. Second, by adding Spanish, Russian, and Italian, our research
covered more language areas in order to check for inter-language diversity and test the
generalizability of the earlier conclusions.6 Third, we systematically used recently introduced
new features of the Google Ngram Viewer such as the option of combining several words into
one graph. In this sense, we scrutinize the results of earlier works applying a more reflexive
and robust methodology to a broader scope of samples.
3.2 Semi-automated search term selection
To fully deploy the new options provided by the enhanced version of the Google Ngram
Viewer, we furthermore had to reappraise the selection of the search terms to be entered into
the Viewer’s search field. So far, authors had mainly focused on how the importance of
function system designations – i.e. terms such as economy, religion, or art – fluctuated in time,
and only gave limited examples of how the performance of pertinent keyword chunks could
be systematically analyzed. To address this limitation, our challenge was to identify the most
pertinent keywords per function system. As the Google Ngram Viewer only allows for a
relatively small number of keywords to be entered into the search field, we limited the
number of desired keywords to five per function system. We hence decided to select the five
most frequent keywords per function system and to combine them into one graph per function
system so as to produce comparative time series plots of fluctuations of the importance of
each function system between 1800 and 2000.
The selection of the five most important keywords per function system was a multistep mix-
methods process. First, we relied on a small collection of Python scripts that generate word
frequency lists based on the Google Ngram dataset (see Annex). In our case, we created lists
of the 10,000 most frequent words per investigated language area. We then manually scanned
these lists for words that refer to one and only one of the 10 function systems, whereby each
list was screened by at least two colleagues. The major challenge in this context was to
identify n-grams that unambiguously refer to not more than one function system. For example,
the n-gram university clearly refers to education, however, not unambiguously so, as it also
refers to science because universities are research institutions, too7; the term does therefore
not qualify as function system indicator, whereas the n-grams money or theory can be
6 We understand some readers might object that we are studying the “Western brain” rather than the global brain;
however, we excluded Chinese because of data quality and linguistic issues that justify to be addressed in a
separate article, and we did not include Hebrew because no team member is proficient in this language. We
opted for 1800-2000 as sample period because the data is most reliable for these two centuries and because this
period corresponds well to our ambition to observe macro trends in modern societies.
7 By contrast, we kept terms such as church or school. Technically speaking, churches or schools are not exactly
mono-functional as we may easily observe power struggles in churches or school fees. Yet, we found that, unlike
the inherently bi-functional universities, churches and schools are relatively strictly coupled to only one
dominant function system.
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relatively safely assumed to refer to economy or science, respectively.8 We then picked the
five most frequent keywords per function system and combined them to strings such as
(business+economic+money+company+cost). If entered into the Google Ngram Viewer,
each such string creates one single graph that represents the combined performance of all
keywords, which in this case presents the combined performance of the five strongest
indicators for the economy.9 As we decided to track the performance of ten systemsnamely
political system, economy, science, art, religion, legal system, sport, health, education, and
mass media system (Roth and Schütz 2015) – we needed to produce two plots of five function
systems each. We repeated the entire procedure for each language and then compared the
results against the subsequent set of hypotheses.
3.3 Hypotheses
As function systems are defined as both dissimilar and equal systems (see Table 1), their
inherent incommensurability makes an excellent case for our null hypothesis. In fact, they
represent coequal nominal data and therefore can be assumed equally relevant to given social
systems. Our null hypothesis, therefore, read as follows:
(H0) The global brain’s memory recalls that all function systems have been equally important
throughout the last centuries. Our global brain wave measurement therefore shows a uniform
distribution of the combined performances of the five most frequent keywords per function
system from 1800 to 2000.
Yet, prominent trend statements and self-definitions of modern societies, such as the ideas of
secularized or capitalist societies, suggest that the global brain recalls unequal distributions
and significant fluctuations of the significance of the individual function systems. Our
alternative hypothesis was as follows:
(H1) The global brain’s memory recalls that all function systems have not been equally
important throughout the last centuries. The combined performances of the five most frequent
keywords per function system therefore exhibit an unequal distribution both in the course of
time (H1.1) and across the language areas (H1.2).
As we intended to pursue the alternative hypothesis and link it to the verification of social
macro trend statements and self-descriptions of modern societies, our second alternative
hypothesis suggested that the global brain’s memory is in line with the most popular common
senses on trends in modern societies:
8 In some cases, we used the Google Ngram Viewer to estimate the degree of word ambiguity. The ngram
company, for example, may also have non-economic meanings such as in “in good company”. Yet, the ratio of
“good company” to “company”, which can be checked using the string (good company/company), is never
exceeding 1.7% and declining to less than 0.3% in 2000. Good is furthermore not among the most common
determiners of company (string: *_DET company). Similarly, “electric power” is almost non-existent in the 19th
century, with the (electric power/power) ratio peaking at hardly more than 1.0% in the 1950s and declining to
less than 0.5% in the 1990s. Power plant or power station also account for less than 0.5% or 0.2 % in 2000.
Again, none of the aforementioned determiners is among the most common (strings: *_DET power and power
*_DET). Wildcard searches such as * company or power * further corroborated our interpretations; they also
proved helpful in contexts such as “pp.”, where we checked that the abbreviation actually refers to the
referencing of book pages and, thus, to the mass media system.
9 It is worth noting that the chunking cancels the recently upgraded feature that unlocks the decision between
case-sensitive and insensitive searches. All our searches were hence case-insensitive searches, which implies that,
for example, the n-grams church and Church be treated as independent search terms.
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(H2) All linguistic regions of the global brain recall significant trends in functional
differentiation, including (H2.1) the secularization, (H2.2) the economization, (H2.3) the
politicization, and (H2.4) the mediatization of society as indicated by the combined
performances of the five most frequent keywords per function system.
4 Results
With the exception of sport,10 the vast majority of the top5 keywords of all function systems
were found within the top2000 of all language areas. The comprehensive list of keywords is
available in Tables A1.1-6 (see annex).
Entering these keywords, we found that the combined occurrence frequencies of these five
most frequent keywords per function system exhibit unequal distributions both in time and
within as well as across all language areas.
Due to the above word or ngram limits to the Google Ngram Viewer input mask, our charts
present five chunks of five keywords. The figures in the running text present the combined
occurrence frequencies of the five most frequent keywords for the for function systems most
relevant to our hypotheses (political system, economy, religion, and mass media)
complemented by the best-performing out of the remaining function systems (Figures 1-6).
Figures showing the performances of all function systems are available in the annex (Figures
A2.1-6).
4.1 English language area
Figure 1: Combined occurrence frequencies of the five most frequent keywords for political system (blue),
economy (violet), religion (orange), mass media (green), and science (red) in the English language Google
Books corpus (1800-2000)
In the English language area, religion is the most dominant function system in the 19th century
and the political system the most dominant one of the 20th century. Starting soon after 1840,
the decline of religion is dramatic and stopped not before World War I. The political system is
the most important function system in the English language area since about 1880; the two
World Wars seem to have had a significant influence on the importance of the political
system. Another smaller peak may be observed in the 1960s. Science became increasingly
important in the 20th century and particularly during the Cold War period; in 2000, science
10 It was not possible to identify unambiguous sport keywords within the first 3000 ngrams of all language areas,
which might be due the relative short (or better: interrupted) history of sport and its consideration as a function
system. Moreover, we did not find even popular sports such as soccer, tennis, or chess among the top10000.
Except for the term sport that actually appeared in the German language area, the ngrams we entered to trace the
performance of sport might be approximations rather than solid indicators until further theory work on sport as
function system. We therefore did not include our presentation of results and discussion; however, we kept the
sport graph in the charts to stimulate feedback and opinions. In any case, sport clearly presents the lowest
importance of all function systems.
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was the second most important systems in the English language area. Originally more
important than science, economy became more important particularly during and between the
two World Wars, but was outperformed by science at about 1950. A small rise of the
information age may be traced back to 1920 with the curve getting steeper since the end of the
1960s. Another significant trend is the considerable uptrend of education since the early 20th
century (see Figure A2.1, annex). In 2000, education enters the top5 after the political system,
science, mass media, and economy. There is also a smaller rise in the importance of health
since the 1960s. The system is seventh after the legal system in 2000. Art and sport are
constantly unimportant throughout the entire observation period.
4.2 Spanish language area
Figure 2: Combined occurrence frequencies of the five most frequent keywords for political system (blue),
economy (orange), religion (green), mass media (violet), and legal system (red) in the Spanish language Google
Books corpus (1800-2000)
The most important function systems in the Spanish corpus are the political system, religion
and the legal system throughout the entire observation period. Religion started as dominant
system in 1800, however, soon displays turbulent interactions with the political and the legal
system, at the end of which religion remains third at about 1870. Yet, there is no dramatic fall
of religion, which again overtakes the legal system in the interwar period and since remained
second until the mid-1970s, and is second just again in 2000. The legal system shows a sharp
decline after 1900, before levelling out at about 1970. Uninterruptedly dominant since about
1840, the peak of the political system is at about 1870, with the systems reaching an almost
similar importance in the 1990s subsequent to a decline that reversed since World War II.
Science (see Figure A2.2, annex) and economy feature little fluctuation throughout most of
the observation period, both featuring a moderate take-off after 1940. After the political
system, religion, legal system, science and economy are fourth and fifth in 2000. A flat
growth curve of the mass media system can be observed to start as early as in the 1860ies.
Originally a fairly prominent function system, health features a significant decline between
1820 and 1880, and is the second least important function system in 2000. Education features
a considerable increase between 1880 and 1910, and art a less pronounced uptrend between
1900 and 1950. Both systems nonetheless belong to the less important function systems in the
Spanish language area.
11
4.3 Russian language area
Figure 3: Combined occurrence frequencies of the five most frequent keywords for political system (blue),
economy (green), religion (violet), mass media (orange), and science (red) in the Russian language Google
Books corpus (1800-2000)
Religion is the most dominant function system in the Russian language area from soon after
1800 to the round about the 1905 Russian Revolution. After a short period of interaction with
the political system in the inter-revolution period, religion declines and is the third least
important system in 2000 despite a small revival since 1990. The political system is the most
dominant function system since the 1917 Russian Revolution, with a steep rise during the
Stalin era to an all-time peak during World War II and two smaller peaks around 1960 and
1980. Science is the second most important system since 1920, and remains in second
position even after a considerable decline between 1980 and the late 1990ies. Between about
1850 and the 1917 Revolution, the legal system was the most important system (see Figure
A2.3, annex). During a short period in the 1820ies the mass media system was second after
religion. Except for this small peak, the mass media system features a stable performance
until a period of modest growth starting after 1950. Since the legal system regained
importance in the context and aftermath of Perestroika, it is the third most important system in
2000, follow by the mass media system and the economy. The latter was virtually absent
before the inter-revolution period, and grew in an only temporary third position between the
mid 1950ies and the early 1990ies. Art, education, religion, and health follow on ranks six to
nine.
4.4 French language area
Figure 4: Combined occurrence frequencies of the five most frequent keywords for political system (blue),
economy (red), religion (green), mass media (violet), and legal system (orange) in the French language Google
Books corpus (1800-2000)
12
The French language area is characterized by an intensive interaction of the legal system,
religion, and the political system until the eve of World War I, when the political system
booms to an all-time high that abruptly skips to a steep decline, after which the system
nonetheless remains in the lead until the end of the observation period. The second peak
around World War II is considerably smaller. After a short period of dominance between
about 1850 and 1870, religion declines to a fifth rank in 2000 (despite a modest revival since
1980), superseded by the legal system, which was dominant until the political boom.
Subsequent to a fairly steep growth curve between 1910 and 1970, the economy is the second
most important system in the French language area in 2000, followed by science (see Figure
A2.4, annex), the legal system and the mass media system. Although quite popular around
1800, art is ranked sixth at the end of the observation period, followed by education, whose
most significant change was a considerable growth between 1830 and 1880. Health is the
least important function system in the French language area.
4.5 German language area
Figure 5: Combined occurrence frequencies of the five most frequent keywords for political system (blue),
economy (red), religion (orange), mass media (violet), and science (green) in the German language Google
Books corpus (1800-2000)
The 19th century in the German language area sees an intensive interaction of religion, legal
system (see Figure A2.5, annex), political system and science. Religion is clearly dominant
and the legal system second until 1860, point of time when the latter starts to dominate until
the eve of World War I leads to a take-off of the political system, which is further fueled
during the Cold War period until a peak at around 1970. Despite a constant yet somewhat
moderate decline, the political system is by far the most dominant system in 2000, followed
by science, which had its 20th century peak around 1970, too. The legal system is fourth and
religion fifth (after having been second between 1940 and 1955). Due to a moderate growth
between 1910 and 1930 as well as the meanwhile stopped declines of religion and the legal
system, the economy remains third in 2000. German language art seemed to be significantly
influenced by the two World Wars, surpassing the economy in both postwar periods, however,
not sustainably so. Education is sixth both after a moderate decline since 1980, tightly
followed by the mass media system. Once quite prominent, health is the least popular function
system in the German language area in 2000.
13
4.6 Italian language area
Figure 6: Combined occurrence frequencies of the five most frequent keywords for political system (blue),
economy (green), religion (orange), mass media (green), and legal system (red) in the Italian language Google
Books corpus (1800-2000)
The first half of the 19th century is characterized by an intensive interaction of a number of
function systems in the Italian language area, too. The dominance of the political system starts
early, however, not uninterruptedly so: Religion is dominant for a shorter period around 1840,
and the legal system for a longer period between 1870 and again the eve of World War I. The
political system peaks during the first and has an interim peak during (and after) the second
World War; a third peak is visible in the 1970ies. Science is second in 2000 after an
undramatic history of modest growth (see Figure A2.6), followed by religion whose decline
since 1840 stopped as early as 1890 and was partly compensated by its post-1980 growth.
Fourth is the mass media system that featured is most significant growth trend between 1850
and 1890 and a second smaller one between 1960 and 1990. The legal system is fifth after two
waves of decline, the first of which started even before the rise of the political system and the
second around 1960. Next is art, whose peaks again correspond to the two World Wars.
Economy is seventh in the Italian language area, followed by education, which featured
continuous growth as of 1800 that ended around 1910. The initially relative high importance
of health soon declined after 1810 and followed a flat degrowth curve displaying two small
dents during the two World Wars.
4.7 Interregional results
The clearest interregional trend is the dominant position of the political system in the 20th
century. This trend applies to all language areas without any reservation other than that it
started already as early as 1880 in some cases, whereas in others the take-off of politics was
not before around World War I.
Most language areas display a 19th-century bounce of religion followed by a significant
decline, which is most pronounced in the English and German case and least in the Spanish
and Italian. All language areas except the Spanish feature an at least moderate revival of
religion starting around 1980.
There are intensive fluctuations and interactions of religion, legal system, and political system
as dominant systems in all language areas in the first half of the 19th century. In the Russian,
French, German, and Italian language area, the legal system was the most dominant function
system in a period between 1870/80 and 1910/20.
Science appears to be particularly important in the English, Russian, and German language
area, in each of which it ranks second since World War I or II.
The economy never had a dominant position in any of the language areas at any point of time.
After featuring a moderate uptrend stagnating in the second half of the 20th century in the
14
larger number of language areas, the economy is the second most important system in the
French area, number three in the German, four in the English and the Italian, and fifth in the
Spanish and Russian in 2000.
Although fairly important in the early 19th century, health is most unpopular in all language
areas by the end of the 20th century, with the only exception being the English where health is
more popular than art since about 1970.
The two World Wars are visible as sometimes tremendous increase of the political graph in
all language areas but the Spanish; there is also a visible influence on art and science in the
German and Italian as well as on religion in the Spanish, French, German, and partly the
Italian case.
It is interesting to note that the Russian language area displays the lowest overall level of
functional differentiation among all areas, particularly during the 19th century. The political
graph of the Spanish chart scores the highest value of all language areas around 1870 and
almost reaches the same level around 1990 again.
In total, we find that each language area has its own distinct profile of function system
preferences. At the same time, there are common trends featured in all or most function
systems, the most striking of which are the dominant position of the political system in the
20th century; the ultimately slightly inverted decline of religion; and the intensive interaction
of religion, legal system, and political system during the second half of the 19th century.
5 Discussion
Our global brain wave measurement shows that the occurrence frequencies of the function
system indicators exhibit an unequal distribution both in time and across all language areas.
According to the data, the global brain recalls that it did not treat the function systems as
equally important throughout the last centuries. The null hypothesis is therefore rejected.
As we further linked the alternative hypothesis to the verification of popular social macro
trend statements, it was our ambition to check whether the global brain recalls significant
trends in all of its linguistic regions. The results clearly indicate that there are trends in all
language areas, which is why our second alternative hypothesis (H2) is also confirmed in its
general form. We therefore proceeded to discuss the data against our sub-hypotheses H2.1-4,
according to which we checked whether the global brain recalls specific social macro trends
such as the secularization (H2.1), economization (H2.2), politicization (H2.3), and
mediatization of society (H2.4).
5.1 Secularization of society, (confirmed)
Starting in the second half of the 19th century, religion presents a strong downtrend in the
English, Russian, and German and a moderate downtrend in the Spanish, French, and Italian
language area. Initially the dominant system in most of the cases, religion is of little
importance in the English and French and of very little importance in the Russia language
area. Yet, the system remains the second most important in the Spanish and the third in the
Italian language area. The German language area shows an ambivalent image: on the one
hand, a dramatic downtrend between 1850 and 1940, on the other hand a short countertrend
temporarily pushing religion back into the second position in the 1940s; another downtrend is
inverted in the 1980, leaving religion on rank four. In fact, as this post-1980 revival of
religion is common to all language areas and, more importantly, as the downtrend is only
moderate in half of the areas, with religion remaining second in one of them, the data seems
to confirm the secularization hypothesis with some reservation only. The global brain seems
to recall periods of clear secularization in some contexts and less clear situations in others. In
any case, the global brain seems to be quite sure that there has been secularization after the
mid-19th century in all language areas. H2.1 may be cautiously confirmed in the end.
15
5.2 Economization of society, rejected
Moderate uptrends of the economy may be observed in all language areas predominantly in
the 20th century. At the end of these processes, which stopped and reversed toward the end of
the century in all cases, the economy makes it to a second rank in the French and a third rank
in the German in 2000 while remaining (even) less important in the other cases. There is not a
single period in a single language area in which the economy has been close to being the
dominant function system. Apparently, the global brain does not recall any situation in which
it has been distracted or even ruled by economic principles. The economization of society
hypothesis is therefore rejected.
5.3 Politicization of society, confirmed
The dominance of the political system is striking in all language areas starting with World
War I at the latest. The distance between the first ranked political system and the second place
systems is enormous in all cases except for the English one. Technically speaking, the English
language area has not been politicized because it already was politicized. Yet, as all other
language areas display a political uptrend, and as all language areas are effectively dominated
by the political system in the 20th century, it is safe to assume that the global brain was
increasingly politicized between 1800 and about 1920 and heavily politicized since then. The
politicization of society hypothesis is confirmed.
5.4 Mediatization of society, (rejected)
The results concerning the mediatization hypothesis are ambivalent. There are visible
uptrends of the mass media indicators in the English (since 1960) and Italian language area
(1850-1890), where the mass media system ranks third and fourth in 2000. We also see a
longer moderate uptrend in the Spanish case since the second half of the 19th century, a
similar trend starting even earlier in the German case, and shorter moderate uptrend in the
Russian case since 1960, too. The French area does not feature a significant trend at all. As
the majority of the curves are comparably flat, and as the final results of the mass media are
not particularly good, we decided to reject the mediatization of society hypothesis with the
reservation that the global brain apparently recalls trends to the mass media system in most
linguistic regions, but still does not hold the system to be particularly important.
5.5. Limitations and future research
As some of our results may appear counterfactual particularly to those colleagues who believe
in a stronger importance of the economy and therefore assume that our method fails to reflect
it, we wish to point at some weak points of our approach.
As much as a physiological encephalography measures electric impulses rather than thoughts
or ideas (thus still giving usable results), so too does our method measure footprints of
communication rather than communication, a circumstance which is further complicated by
the fact that we observed word frequencies without word contexts. Although the wild card
search option of the Google Ngram Viewer allowed for simple context checks, the ideal case
might be a research design for the analysis of 2- or more-grams. Yet, a noncontingent
selection of key phrases rather than keywords per function system required access to superior
IT infrastructure as already the extraction of the top10000 word list required several hours per
list and the effort for even just 2gram lists would be dramatically higher.
Another methodological bias in our approach was our focus on only five keywords per
function system, which was necessary because the of the above limits to simultaneous search
entries into the Google Ngram Viewer. This approach systematically disfavored the stronger
function systems that feature not only the more frequent but also simply more keywords in the
16
word frequency lists. Particularly the dominance of the political system might therefore even
more pronounced if we had means to trace the combined performances of all political
keywords, whereas we do not have any evidence that the relative performance of the economy
would be increased if we combined all economic keywords.
Despite these considerable limitations, we are confident that our research is solid enough to
present a reliable approximation to the relative importance of the function systems in the
observed language areas. In fact, our global brain wave measurement was able to capture
many significant historical events and trends such as the secularization, the Russian
Revolution, the World Wars, or the moderate information and wellness trend(s) in the
concerned language area(s). In fact, the only counter-intuitive result in our research is the
mediocre importance of the economy, and criticism of our method would have to address the
question why the method was able to capture secularization and politicization while
simultaneously failing to display the true importance of the economy and, in doing so, make
proposals of how the importance of the economy may be better identified on such a large
scale.
The major challenges for future global brain wave measurements will be
1) The cross-validation of the method systematically exploring interactions between the
charts and established historical knowledge in the respective language areas,
2) The inclusion of the missing language areas, with a particular challenge being the
Chinese where both OCR issues and specifies of the Chinese language need to be
addressed,
3) A cross-media integration allowing for the combined analysis of book and Internet
data particularly in view of the post-millennium period,
4) The development of research designs that allows for the trending of combined system-
specific more-grams, although it is not clear yet whether and how keyword
combinations and sentences might be better indicators than keywords or how word
context may be better captured by any other means, respectively,
5) The integration and development of research designs for the anticipation of future
social mega trends in functional differentiation.
We believe that this effort is justified and worthwhile simply because it may be used as an
explorative tool that helps with generating research questions. Moreover, there is clear
evidence that the data are much more than random. We can all see the tremendous impact of
the two World Wars on the importance of function systems in general and the political system
in general. Another striking result is the intensive fluctuations and interactions of religion,
legal system, and political system in the 19th century as well as the subsequent interregnum of
the legal system, which corresponds to works by Thornhill (2008; 2010). Even more specific
results such as the relationship between political and legal system in the Russian language
area between 1900 and 2000 interact well with pertinent research on the evolution of law
under (post-) communism in Eastern Europe (Schönfelder 2016).
Against this backdrop, the method is definitely useful to challenge our overconfidence in
traditional trend statements or definitions such as the truism that modern societies are
economized or capitalist.
6 Conclusion
In this article, our ambition was to verify popular social macro trend statements and to review
how reasonable it is to use these statements to characterize modern societies. To this end, we
performed a global brain wave measurement in the form of word frequency analyses in the
largest online body of human knowledge to screen the global brain’s memory for traces of a
17
secularization, an economization, a politicization, and a mediatization of society in six
language areas from 1800 to 2000.
The results suggest that modern societies – as far as they belong to the English, Spanish,
Russian, French, German or Italian language area can be appropriately defined as
politicized societies as of World War I. It is furthermore appropriate to highlight
secularization as mega trend, although the data also suggest that this trend was most
significant in the second half of the 19th century and partly reverted in the late 20th century.
We might even still continue to speculate that there is an emerging mega trend towards an
information society as at least some language areas feature a considerable uptrend of the mass
media system towards the end of the observation period. What is not supported by our data,
however, is the idea that modern societies are dominated by the economic system. Definitions
of modern societies as capitalist societies therefore appear untenable as long as the
corresponding definitions of capitalism imply an over-average importance of the economy in
one way or another. In fact, the global brain recalls that there is not a single region of its
memory in which the economy was dominant at any point of time, and that only one of its
region corresponds to a capitalist profile in terms of a society dominated by money and power.
Ironically, this case is the French language area. Traditional Marxist or other “political-
economic” definitions of capitalism may hence still be applied to the French special case.
With regard to all other language areas, it appears that classical critical theory is more
appropriate insofar as it accounts for the high importance of what it refers to as scientific-
technological rationality in modern societies, which actually is reflected by our data. Yet,
even critical theories fall short of accounting for the significance of other important function
systems, and this holds also true for more fashionable variants of the critical political-
scientific-economic gaze such as the triple helix concept, which is obviously dominant not
least in foresight and futures studies (Roth and Kaivo-oja 2015) although some of its most
prominent promoters have already stressed the need to considerably broaden the concept
(Leydesdorff 2012; Leydesdorff 2013). Against the background of our data, the neglect of
religion is particularly striking since the global brain recalls that, in 2000, the system was
second in the Spanish and Italian language area and more important than the mass media in all
language areas but the English and the Russian.
It must hence be asked whether research in the pasts, presents, and futures of the world
society may afford to remain so strongly focused on the political system, science, and the
probably only marginally important economy, thus ignoring influences from other potentially
more relevant function systems.
As this critical question emerged in the context of research on the global brain conducted in
this global brain, this question literally is a question asked and to be answered by the global
brain; and there is hope that the reflection stimulated by such questions eventually increases
the reflexivity up to a point where a recalibrated self-concept enables the global brain to
critically review, anticipate, and influence mega trends (Heylighen and Lenartowicz 2016).
The starting point to the millionfold claimed and desired shift to a post-capitalist society (Last
2016) may hence be in a global brain that takes its own memories seriously and therefore
widely ignores local obsessions with economic issues, thus taking the liberty to concentrate
on more important matters. One not undesired side effect of this refocus would be a science
more aware of its actually prominent role in society using its prominence to redirect its
recalibrated self-esteem and methodologies to important issue, which are not primarily in the
economy, but rather in the political system and religion, and not least, in science itself.
18
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Annex
Tables A1-6 Keywords per language area
System
English
Frequencies/chunk
Political
(power+government+States+political+war)
545937001
Science
(system+method+theory+research+analysis)
346647669
Mass Media
(information+pp.+book+Press+published)
315167212
Religion
(God+St.+Church+church+religious)
300982802
Economy
(business+economic+money+company+cost)
300208287
Legal
(law+property+Court+rights+laws)
266353906
Education
(school+education+students+schools+learning)
242819722
Health
(health+disease+patients+medical+Health)
154036473
Art
(art+music+style+beautiful+Art)
143214993
Sport10
(success+failure+successful+failed+game)
119162189!
Table A1.1: Top five keywords plus ranked combined keyword frequencies per function system in the English
language Google Books sub-corpus.
System
Spanish
Frequencies/chunk
Political
(Estado+política+gobierno+poder+Gobierno)
134852140
Legal
(derecho+derechos+Ley+leyes+propiedad)
75981764
Religion
(San+Dios+Santa+Iglesia+fe)
74476545
Science
(sistema+verdad+análisis+ciencia+teoría)
56072336
Economy
(económica+económico+comercio+empresas+empresa)
49330531
Mass Media
(libro+información+libros+Revista+edición)
38558611
Education
(educación+enseñanza+escuela+Escuela+Educación)
31905924
Art
(poeta+poesía+música+poema+belleza)
20910042
Health
(salud+enfermedad+médico+enfermedades+médicos)
19128267
Sport10
(éxito+juego+fracaso+juega+deporte)
11839655
Table A1.2: Top five keywords plus ranked combined keyword frequencies per function system in the Spanish
language Google Books sub-corpus.
System
Russian
Frequencies/chunk
Political
(СССР+войны+власти+США+государства)
76946803
Science
(системы+исследования+наук+теории+науки)
59039008
Economy
(предприятий+предприятия+экономической+экономически
х+экономического)
29734309
Mass Media
(книги+информации+печати+книге+Библиогр)
26339208
Legal
(право+закона+собственности+суда+закон)
21365792
Art
(искусства+поэта+искусство+поэзии+стихи)
18387362
Education
(школы+ школе+обучения+учащихся+студентов)
18038985
Religion
(церкви+церковь+Бога+бог+религии)
11682835
Health
(больных+болезни+здоровья+лечения+заболевания)
9884313
Sport10
(играет+играют+играл+игра+игру)
5245216
Table A1.3: Top five keywords plus ranked combined keyword frequencies per function system in the Russian
language Google Books sub-corpus.
21
System
French
Frequencies/chunk
Political
(politique+guerre+gouvernement+Etat+liberté)
128660182
Legal
(loi+droits+lois+justice+propriété)
95442257
Religion
(Saint+Dieu+âme+religion+saint)
81371288
Economy
(prix+commerce+économique+économie+entreprise)
71997017
Science
(système+vérité+science+analyse+méthode)
68356691
Art
(art+Art+beau+belle+musique)
54578746
Mass Media
(livre+livres+Revue+Journal+publié)
41145107
Education
(enseignement+école+examen+education+écoles)
40349608
Health
(maladie+malade+santé+maladies+maladies)
29691319
Sport10
(succès+jeu+jouer+échec+échoué)
22549308
Table A1.4: Top five keywords plus ranked combined keyword frequencies per function system in the French
language Google Books sub-corpus.
System
German
Frequencies/chunk
Political
(politischen+Regierung+Staaten+Politik+Staat)
62414914
Legal
(Recht+Gesetz+Rechte+Gesetze+Gesetzes)
40407209
Science
(Wissenschaft+System+Theorie+Philosophie+Wahrheit)
40078241
Religion
(Kirche+Gott+Gottes+Seele+Religion)
36619670
Education
(Bildung+Schule+Ausbildung+Schüler+Lehrer)
25699368
Economy
(Wirtschaft+Kosten+wirtschaftlichen+Unternehmen+Geld)
24474056
Mass Media
(Buch+Verlag+Zeitschrift+Hrsg.+Zeitung)
21346370
Art
(Kunst+Dichter+Musik+Künstler+Schönheit)
20451747
Sport10
(Erfolg+gewonnen+spielen+Spiel+Sport)
13180185
Health
(Krankheit+Patienten+Kranken+Arzt+Krankheiten)
10590675
Table A1.5: Top five keywords plus ranked combined keyword frequencies per function system in the German
language Google Books sub-corpus.
System
Italian
Frequencies/chunk
Political
(Stato+politica+guerra+governo+potere)
50776746
Legal
(legge+leggi+proprietà+contratto+giudice)
36448377
Mass Media
(cit+pag+libro+stampa+pubblicazione)
30276212
Science
(sistema+ricerca+verità+scienza+filosofia)
29094653
Religion
(Dio+Chiesa+San+chiesa+religione)
23916703
Economy
(lire+spese+economica+commercio+economico)
19951296
Art
(arte+poesia+poeta+disegno+musica)
17599946
Education
(scuola+scuole+educazione+insegnamento+Scuola)
10291174
Health
(malattia+medico+salute+malattie+medici)
7626616
Sport10
(successo+gioco+fallimento+fallito+giochi)
5424142
Table A1.6: Top five keywords plus ranked combined keyword frequencies per function system in the Italian
language Google Books sub-corpus.
22
A2 Additional figures
Figure A2.1: Combined occurrence frequencies of the five most frequent keywords for all ten function systems
in the English language Google Books corpus (1800-2000)
23
Figure A2.2: Combined occurrence frequencies of the five most frequent keywords for all ten function systems
in the Spanish language Google Books corpus (1800-2000)
24
Figure A2.3: Combined occurrence frequencies of the five most frequent keywords for all ten function systems
in the Russian language Google Books corpus (1800-2000)
25
Figure A2.4: Combined occurrence frequencies of the five most frequent keywords for all ten function systems
in the Russian language Google Books corpus (1800-2000)
26
Figure A2.5: Combined occurrence frequencies of the five most frequent keywords for all ten function systems
in the German language Google Books corpus (1800-2000)
27
Figure A2.6: Combined occurrence frequencies of the five most frequent keywords for all ten function systems
in the Italian language Google Books corpus (1800-2000)
28
A3 Examples from the collection python script to generate word frequency lists based on
Google Ngram datasets by Jan Berkel, available under CCA 3.0 Unported License (CC-
BY) at https://gitlab.com/jberkel/google-ngram-word-frequency-lists/tree/master
A3.1 google_word_frequency.py
#!/usr/bin/env python
""" Creates top-N frequency lists based on Google's Ngram datasets published on
http://storage.googleapis.com/books/ngrams/books/datasetsv2.html
Only 1-grams are taken into account.
"""
from google_ngram_downloader import readline_google_store
from itertools import groupby
from string import ascii_lowercase
import json
import sys
VALID_LANGUAGES = ['fre', 'ger', 'eng', 'eng-us', 'eng-gb', 'eng-fiction',
'chi-sim', 'heb', 'ita', 'rus', 'spa']
DEFAULT_N = 3000
MIN_YEAR = 0
MAX_YEAR = 2012
FILTER_THRESHOLD = 3000
def all_records(lang, indices=[x for x in ascii_lowercase], verbose=True):
"""Returns a generator producing all records for language & indices"""
if lang not in VALID_LANGUAGES:
raise Exception('Invalid language %s' % lang)
for _, _, records in readline_google_store(ngram_len=1, lang=lang,
indices=indices,
verbose=verbose):
for record in records:
yield record
def count_ngrams(grouped_records):
return ((ngram, sum(r.match_count for r in records))
for ngram, records in grouped_records)
def filter_high_frequency(ngram_counts, threshold):
return ((word, count) for (word, count) in ngram_counts
if count > threshold)
def record_filter(record, config):
ret = record.year in range(config.startyear, config.endyear+1)
29
if config.debug and ret:
print("matched: %s" % str(record))
return ret
def ngram_input(config):
records = all_records(config.lang, verbose=config.debug)
return (r for r in records if record_filter(r, config))
def top_n_words(n, records, threshold=FILTER_THRESHOLD):
"""returns the n most frequent words for a given language"""
return sorted(list(filter_high_frequency(
count_ngrams(groupby(records, key=lambda r: r.ngram)), threshold)),
key=lambda word_count: -word_count[1])[:n]
def write_json_file(words, config):
timespec = "-y%d-%d" % (config.startyear, config.endyear)
filename = "wordlist-%s-n%d%s.json" % (config.lang, config.n, timespec)
with open(filename, 'w') as f:
json.dump(words, f, indent=True)
def parse_config(args):
from argparse import ArgumentParser
parser = ArgumentParser(
description='Extract top frequency words from Google Ngram data')
parser.add_argument(
'-n', dest='n', type=int, default=DEFAULT_N,
help='number of words to extract (default %d)' % DEFAULT_N)
parser.add_argument(
'--startyear', dest='startyear', type=int, default=MIN_YEAR,
help='only include words after specified year (default %d)' % MIN_YEAR)
parser.add_argument(
'--endyear', dest='endyear', type=int, default=MAX_YEAR,
help='only include words up to specified year (default %d)' % MAX_YEAR)
parser.add_argument(
'--lang', dest='lang', required=True, choices=VALID_LANGUAGES,
help='language code')
parser.add_argument('--debug', action="store_true")
config = parser.parse_args(args)
if config.startyear < 0:
raise Exception('invalid startyear, must be > 0')
if config.endyear > MAX_YEAR:
raise Exception('invalid endyear, must be < %d' % MAX_YEAR)
if config.endyear < config.startyear:
raise Exception('endyear < startyear')
return config
30
if __name__ == '__main__':
config = parse_config(sys.argv[1:])
top_n = top_n_words(config.n, ngram_input(config))
write_json_file(top_n, config)
A3.2 post_process.py
#!/usr/bin/env python
""" Parses word lists, and performs some prepocessing and cleanup
The processed list is written to stdout.
"""
import json
import re
from collections import OrderedDict
POS_PATTERN = re.compile("_[A-Z]+$")
def sort_list(word_list):
return sorted(word_list, key=lambda word_count: -word_count[1])
def deduplicate(word_list):
result = OrderedDict()
for (word, count) in word_list:
if word not in result:
result[word] = count
return list(result.items())
def strip_pos_tags(word_list):
def strip_pos_tag(word):
matcher = POS_PATTERN.search(word)
if matcher:
return word[:matcher.start()]
else:
return word
return [(strip_pos_tag(word), count) for (word, count) in word_list]
def normalize(word_list):
return deduplicate(sort_list(strip_pos_tags(word_list)))
if __name__ == '__main__':
import sys
31
import codecs
with sys.stdin as f:
writer = codecs.getwriter('UTF8')(sys.stdout)
writer.write(json.dumps(normalize(json.load(f)), indent=True))
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... The findings of Roth et al. (2017), which were largely confirmed in a replication study that used a more sophisticated methodology (Roth et al., 2019) suggest that too narrow a focus on one or few macro trends might result in reductionist visions of society and that a particular theory's predilection for certain social systems may correspond more with some epochs than with others. Although scholars may differ on whether the turning point from political economy to political science occurred soon after WWII or in the 1970s, it appears safe to assume that such trends and turning points exist, that they affect the plausibility of theories, and that the current coronavirus crisis is creating a situation in which a hitherto less prominent function system-namely, health-will become much more significant. ...
... Social macro trends in the Google Books corpus (1800-2000) (Source:Roth et al., 2017). ...
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We analyze the role of the Global Brain in the sharing economy, by synthesizing the notion of distributed intelligence with Goertzel's concept of an offer network. An offer network is an architecture for a future economic system based on the matching of offers and demands without the intermediate of money. Intelligence requires a network of condition-action rules, where conditions represent challenges that elicit action in order to solve a problem or exploit an opportunity. In society, opportunities correspond to offers of goods or services, problems to demands. Tackling challenges means finding the best sequences of condition-action rules to connect all demands to the offers that can satisfy them. This can be achieved with the help of AI algorithms working on a public database of rules, demands and offers. Such a system would provide a universal medium for voluntary collaboration and economic exchange, efficiently coordinating the activities of all people on Earth. It would replace and subsume the patchwork of commercial and community-run sharing platforms presently running on the Internet. It can in principle resolve the traditional problems of the capitalist economy: poverty, inequality, externalities, poor sustainability and resilience, booms and busts, and the neglect of non-monetizable values.
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This article tests whether the field of foresight and futures studies shows significant variable selection biases in the modelling of the future in general and the impact of function systems in particular. We performed a word frequency analysis to measure the relative importance of the political system, the economy, science, art, religion, law, sport, health, education, and the mass media to three pertinent journals in the field of futures studies and foresight. The results show that Futures, Long Range Planning, and Technological Forecasting and Social Change have different and changing preferences for the above function systems, an information which authors may find helpful in supporting decisions on where to submit. Our results also show that all journals feature a highly significant bias to the triple helix systems – the political system, the economy, and science. While the latter bias may be adequate to scientific journals, the dominant focus on the political system and the economy as well as the corresponding neglect of the other systems points at implicit presumptions about the importance of the individual systems that may not be in line with their importance to the larger society. Highlights: This article • Shows that present visions of futures are predominantly visions of political economies, and how to change this. • Suggests that solutions to future political and economic key problems might also be in the so-far neglected further function systems. • Proposes a new systematic set of key variables for consideration and inclusion in models and simulations of futures.