The Development of Computer Science: A Sociocultural Perspective

1.1.Background1
This thesis deals with the sociocultural dimensions of computer science.  During the
last 60 years, researchers in the academic field of computing2 have brought together
a variety scientific  disciplines and methodologies.  The resulting interdisciplinary
science, computer science, offers a variety of ways of modeling and explaining phe-
nomena, such as computational models and algorithms.  The growth of research ef-
forts in computer science has been paralleled by the growth of the number of com-
puting-related fields, such as computer engineering, computational science, electrical
engineering, decision support systems, architectural design, and software engineer-
ing.  Computers and information and communication technologies at large have also
enabled a world of new socioeconomic and cultural concepts such as  e-commerce,
hacker ethics, the knowledge economy, discussion boards, and virtual communities
to emerge.
There is a wealth of research on the sociocultural impact of computers and commu-
nication technologies—studies of the new economy, working culture, leisure time,
new social formations, e-economy, and so forth3.  Those studies most often focus on
if and how new computing (and communication) technologies affect society and cul-
ture,  and they also focus  on the  interactions of technology,  society,  and culture.
There is less research on how sociocultural influences affect the development of the-
ories, techniques, and instruments in computer science.
According to naïve technological determinism, technology develops independently
of society4.   Yet,  science and technology studies (STS)5 scholars generally reject
naïve technological determinism.  They often argue that the directions that research
and development take are frequently decided on by coteries external to science, and
1 Parts of this introduction are from Tedre et al., 2006.
2 The term computer science was introduced long after the construction of the first fully electronic, digital, Turing-
complete computers.  The history of computer science as a discipline is discussed in Chapter Three.
3 Sociologist Manuel Castells has characterized various aspects of the network society in his trilogy The Informa-
tion Age: Economy, Society, and Culture (Castells, 1996; Castells, 1997; Castells, 1998).
4 MacKenzie and Wajcman, 1999 :p.xiv.
5 Science and technology studies (STS) is often used as an umbrella term, which includes a number of research
areas and approaches that concern science and technology: for example, the philosophy of science, studies of the
social construction of technology (see Kline and Pinch, 1999), the sociology of scientific knowledge (SSK) (see
Barnes et al., 1996), and the history of science.
2
that technological decisions are often based on, for instance, economic, political, or
ideological arguments rather than technological arguments6.  
Several  motivations  can  be  attributed,  for  example,  to  the  development  of
GNU/Linux and its introduction into use7.   Arguably, GNU/Linux is advanced (a
technical motivation), it is free of initial investment (an economical motivation), and
its roots are in hacker ethics and the free software movement (ideological and social
motivations).  Also, sometimes it can emphasize a cultural or political message (e.g.,
IMPI Linux in South Africa has its roots in the concerns of “digital colonialism”,
and RedFlag Linux in China has its roots in government support for an independent
operating system).
The impetus for studies of the connections between technology, academy, institu-
tions, sciences, social milieux, human practices, economical concerns, agenda, ideo-
logies, cultures, politics, arts, and other technological, theoretical, and human aspects
of the world, arose in the wake of the constructionism of the 1960s.  For instance,
the strong programme in the sociology of scientific knowledge adopted the view that
all beliefs, including scientific ones, are influenced by their sociocultural surround-
ings8; the philosophy of science took a new, social constructionist turn9; the univer-
salist, positivist nature of mathematics was undermined10; historians of science and
technology rejected inevitabilism and determinism11; and in the 1980s a new field
called  science and technology studies, which focuses on the social construction of
science and technology, was formed12.
Scientists in a number of disciplines have augmented their disciplinary understand-
ing by exhaustive research on the methods, motives, and stakeholders' roles in their
respective fields.  Science and technology have been the subject of investigation of
philosophers such as Karl Popper, Thomas Kuhn, Paul Feyerabend, Martin Heideg-
ger, José Ortega y Gasset, and Imre Lakatos; of sociologists such as David Bloor,
6 See, e.g., Bijker and Law, 1992; MacKenzie and Wajcman, 1999 ; Smith and Marx, 1994; Pinch & Bijker, 1987;
Bijker et al., 1987.
7 This GNU/Linux example is from Tedre et al., 2006.
8 One of the original works on the strong programme in the sociology of scientific knowledge is David Bloor's
Knowledge and Social Imagery (Bloor, 1976).
9 Kuhn, 1996 (orig. 1962)
10 Lakatos, 1976; Barnes et al., 1996
11 Hughes, 1983; Kuhn, 1996; Heilbroner, 1967; Marcuse, 1964
12 Two influential books on social construction of technology are MacKenzie and Wajcman, 1999  (1st edition was
published 1985) and Bijker et al., 1987; other similar collections are Bijker and Law, 1992 and Smith and Marx,
1994.
3
Donald  MacKenzie,  Bruno  Latour,  and  Barry  Barnes;  and  of  historians  such  as
Thomas P. Hughes and Lewis Mumford.  Those authors, among others, have offered
a variety of interpretations of why scientists are doing things as they are, why discip-
lines have shaped as they have, and what kinds of interconnections there are between
disciplines,  technologies,  individuals,  institutions,  and  other  influential  actors.
Those explanations, or descriptions, are called descriptive accounts of science.
In addition, each new turn in the disciplinary self-understanding of a particular dis-
cipline has brought with it changes in prescriptions of how scientists in that discip-
line should work.  For instance, Karl Popper's refutation of logical positivism13 refor-
mulated the conception of good scientific practice, and Thomas Kuhn's work gave
impetus to the science wars14.  New viewpoints of how science should be done have
influenced, for instance, the methodologies, ethics, and epistemologies of science.
Those prescriptions, or recommendations, are called normative accounts of science.
Although computer science is an innately interdisciplinary discipline, and although
computer science is used as a tool for a variety of disciplines, there is uncertainty
whether research that focuses on computer science and computer scientists belongs
to the field of computer science.  Even though there are an increasing number of
studies that might be characterized as social studies of computer science, those stud-
ies are not clearly recognized as computer science.  For instance, meta-research on
computer science does not have a clear place in the ACM classification system for
computing research15.  In this thesis I argue that insight into the sociocultural aspects
of the creation, maintenance, and modification of computer science (as a theoretical,
conceptual, practical, and technical framework) is an essential part of computer sci-
ence and should be included in the charter of computer science itself.
13 Popper, 1959 (orig. 1935)
14 See Kuhn, 1996 (orig. 1962).  Much of the 1990s' intellectual scene was characterized by the debate between re-
lativist thinkers who criticized the objectivity of science and realist thinkers who defended the objectivity of sci-
ence (see, e.g., Bucchi, 2004, especially Chapter 6).  This debate was dubbed the science wars, and even though
the debate has lost its media appeal, neither side has given up the dispute.  Most people, like me, do not admit to
belong to either group.
15 See http://www.acm.org/class/1998/ (accessed September 27th, 2006).
4
My argument starts with an explication of a conceptual framework16 for this thesis
(Chapter Two).  My conceptual framework is typical of science and technology stud-
ies, and it derives from a number of theories in science and technology studies.  I use
that framework in my analysis of reports about the emergence of the discipline of
computing and in my analysis of the formation of the disciplinary identity of com-
puter science (Chapter Three).  Based on my interpretation of the formation of com-
puter science, and on the argument that social studies of science should be a part of
the project of science itself17,  I propose that social studies of computer science is an
important part of computer science and I outline the disciplinary implications of my
proposal (Chapter Four).  Before my argument begins, however, I discuss the back-
ground of my research, specify my research questions and research methodology,
outline the structure of this thesis, and explicate the limitations and assumptions un-
derlying my thesis.
Computation
The term computation in this thesis does not refer to the dictionary definition18, but
rather to how computation (intuitively, albeit vaguely) is often understood among
computer scientists—that is, the implicit or explicit execution of algorithms19.  This
notion shifts the focus to questions such as “What is an algorithm?” and “What does
executing an algorithm mean?”.  Algorithm can be defined as a finite set of instruc-
tions that operate on a finite set of symbols and can be, at least theoretically, imple-
mented on some mechanism20.   Executing  an  algorithm entails  simply following
those instructions.  
The term algorithm can be defined further by arguing that algorithms should be real-
izable.  It can be argued that in order to be realizable, an algorithm has to be unam-
biguous21.  It can also be argued that an algorithm has to be useful, so it can have in-
16 By conceptual framework, I refer to the terminology, concepts, models, and characterizations of processes that
are used to explain and predict a certain phenomenon.  As there is significant disagreement about even funda-
mental concepts such as ontology, my conceptual framework defines the language used in this thesis.  This thesis
does not have a theoretical framework, because I have not been able to find a single theory able to fully charac-
terize computer science.
17 Barnes et al., 1996:p.iix.
18 To determine by mathematics, especially by numerical methods; to determine by the use of a computer (AHD,
2004) (see p. 161 of this thesis).
19 Scheutz, 2003
20 Scheutz, 2003
21 See Knuth, 1968:pp.5-6 about algorithms,  computational methods (algorithms, which may lack finiteness), and
reactive processes (nonterminating computational methods, which interact with their environment).
5
put and it must have output.  In addition, algorithms can be required to finish in a fi-
nite time, or, stricter, that the execution of an algorithm finishes within some sens-
ible time limits.
Note that this characterization of  computation does not define the  mechanism  that
does the computing.  As long as the mechanism is realizable, it can be concrete or
abstract, it can be real or imagined, and it can be natural or artificial.  The mechan-
ism can be based on gears, thinking, pneumatics, magic, or hamburgers—or it can be
based on an electronic computer22.   So,  computation does not commit one to just
computation with computers.  One can study computation without any connection to
any existing or future machinery.  Matthias Scheutz noted that a characterization of
computation as executing algorithms that are realizable, useful, and finite does not
yet commit one as to what computations are about or what computations are sup-
posed to achieve23.  However, in practice computation is often connected with some
practical realms and functions, such as information and automation.
Issues such as what computation is about, what computation is supposed to achieve,
and other problems that computation brings about are discussed throughout this thes-
is.  In this thesis the term the study of computing refers to academic or non-academic
studies on computation and the immediate phenomena24 surrounding them, such as
mechanical implementations, users, theories, data, and information.  The term com-
puter science refers to the academic discipline concerned with computation and its
surrounding phenomena (I analyze the problems with the term computer science in
detail in Section 3.4).  In Section 3.4 I also discuss the problems involved in making
a distinction between computer science25, computing as a field26, and computing as a
discipline27.
There is a plethora of concepts, definitions, and topics as well as problems, contro-
versies, and juxtapositions connected with computation.  Figure 1 maps a subset of
the computation-related concepts that are discussed in this thesis.  The concepts in
22 cf. Dijkstra, 1987.
23 Scheutz, 2003
24 Note that the term phenomenon is used in at least two meanings.  The first of these meanings is “an occurrence,
circumstance, or fact that is perceptible by the senses.” (AHD, 2004).  The second of these meanings comes from
Immanuel Kant, and it refers to an object as it is perceived by the senses, as opposed to a noumenon.
25 Newell et al., 1967
26 Denning et al., 2001:p.12.
27 Denning et al., 1989
6