DHQ: Digital Humanities Quarterly
2012
Volume 6 Number 2
Volume 6 Number 2
Towards a Conceptual Framework for the Digital Humanities
Abstract
The concept of a great scientific domain broadens what is normally considered to be within the purview of science while identifying four such domains – the physical, life, social and computing sciences – and suggesting that the humanities naturally fit within the sciences as part of an expanded social domain. The relational architecture that has been developed to aid in understanding disciplinary combinations across great scientific domains then guides an exploration of the structure and content of the digital humanities in terms of a space of relationships between computing and the humanities.
Introduction
For roughly a decade (1998–2007) I led new directions activities at the University of
Southern California’s Information Sciences Institute across the domain of computing
and its interactions with engineering, medicine, business, and the arts &
sciences. Reflections on this extended multidisciplinary experience have led to the
articulation of a new perspective on the nature and structure of computing as a
scientific discipline [Rosenbloom 2004]
[Rosenbloom 2009]
[Rosenbloom 2010]
[Rosenbloom 2012]
[Denning 2009]. In the process has come: a new conception of what a
great scientific domain is; the realization that computing forms the
fourth such domain, with the physical, life, and social sciences comprising the other
three domains; the recognition that much of the core content and future of computing
is inherently multidisciplinary; the understanding that this multidisciplinarity can
be reduced to a small fixed set of across-domain relationships, defining the
relational architecture; the demonstration that the relational
architecture yields a novel organizational framework over computing; and the
application of this framework to illuminating some of the connections between
computing and other scientific disciplines. It has also suggested several tentative
conclusions concerning disciplines outside of computing, such as that mathematics and
the humanities can both be considered as part of the scientific enterprise, but that
neither amounts to a great scientific domain on its own. Mathematics instead nestles
naturally within a broad understanding of the computing domain, while the humanities
fit within a comparably broad understanding of the social domain.
The purpose of this article is to further explore these notions with respect to the
emerging area of the digital humanities, with their focus on the
interchange between computing and the humanities. In particular, we will look at the
idea that the humanities can be viewed as a part of science — in fact, as part of the
social domain — and at the framework that this yields for understanding the space of
relationships between computing and the humanities. Such an exploration requires some
understanding of computing, the humanities, and the philosophy of science. I am a
professional within the first of these, but no more than an interested amateur with
respect to the latter two. So there are inherent risks in this enterprise, but the
hope is that the utility of its results will overbalance any naiveté exposed in the
process.
The Humanities as Social Science
As I have been reflecting on computing, and following the resulting implications
where they lead, I have come to accept the notion that any enterprise that
tends to increase our understanding of the world over time should be
considered as essentially scientific, and thus part of science. This is more akin to
Lakatos’s concept of a progressive research programme
[Lakatos 1978] than to Popper’s focus on falsifiability
[Popper 1959], although not limited by Lakatos’s conception of the
necessary role of correct predictions in establishing progressiveness. The ability to
make correct predictions provides one means of assessing whether the world is better
understood over time, but it need not be the only way. For example, development of a
simple theory whose scope and predictions are comparable to those of a more complex
extant theory may also provide an increase in understanding even without yielding
additional correct predictions.
On the flip side, it is important that a scientific enterprise also not tend to
increase our misunderstanding over time. Fortune telling and religious prophecy can
lead to correct predictions, particularly when ambiguity is combined with generous
post-hoc interpretations and rationalizations, but neither has demonstrated any
ability to predict better than random guessing. On the whole, they thus can
contribute much more to misunderstanding than to understanding despite occasional
hits. Such activities are clearly not scientific. But, while the difference is clear
here, there can be grey areas where it is difficult to determine whether some
activity tends to increase our understanding. Work on a normative scientific
method attempts to deal with this by prevalidating the approach taken to
understanding. As long as the scientific method is used, what comes out of it will be
science. The problem, of course, is that much of actual science does not proceed by
such a method, and it would be greatly impoverished if it were forced to do so.
Moreover, much trivial science follows the method to the letter.
When I read a scientific article, what I care about is learning something new and
important that I can be convinced is true (or at least plausible enough to be worth
considering further). I am agnostic, at least in principle, concerning what methods
were used to invent or discover the new thing, or what methods were used to convince
me of their reality, as long as they achieve the desired ends. Great science requires
all three of these attributes: novelty, importance and veracity. Good science makes
some compromises. To many researchers, all that is important for good science is
veracity, and work may be publishable with miniscule quantities of the other two
attributes as long as there is sufficient evidence of — or methodology for
establishing — truth. In contrast, I often learn more from sufficiently novel and
important conjectures — even before there is a great deal of evidence or methodology
in their favor — leaving me more comfortable in labeling such papers as good science
than traditional small-but-validated results. I don’t learn of necessary truth from
such articles, but they may still revolutionize my way of thinking about a topic,
opening up new possibilities and plausibilities never previously considered. This is
a form of increase in understanding more akin to that emphasized by Kuhn in his work
on scientific revolutions, and that is absolutely crucial to long-term progress in
science [Kuhn 1962].
This distinction is actually reminiscent of an interchange in Austen’s (1818) Persuasion, between Anne Elliot and Mr. Elliot, concerning
the nature of good company. The former prefers “the company of
clever, well-informed people, who have a great deal of conversation,” while
the latter states that “Good company requires only birth,
education, and manners, and with regard to education is not very nice.” Mr.
Elliot then goes on to state of Anne’s notion that “that is not
good company; that is the best.” What is “best” in both company and
science is that which improves understanding, whether based on novel facts or new
ways of thinking. The forms are of secondary importance at best.
The scientific method provides a validated approach for developing insights, but it
is not the only method, nor necessarily always the most appropriate method. For any
particular domain, and any particular problem within that domain, there may be zero,
one, or more methods applicable to them. If we define the strength of a
method for a domain or problem as the degree of veracity it can guarantee for the
results it generates, good science should in general be pursued via one from among
the strongest applicable methods. Using weaker methods when stronger ones are
available can be one of the hallmarks of bad science. We need to be careful though
about what is meant by two methods being applicable to the same problem, and thus the
circumstances under which a stronger method should necessarily dominate. If two
methods can yield the same insights, and one provides more assurance with respect to
these insights, then the two are applicable to the same problem and the stronger
should be preferred. However, if the problems are nominally the same but the two
methods provide different insights about it, then the weaker method may still be of
value, and the problems they tackle are in an important sense different.
The potential diversity of appropriate methods, both within and across domains, does
suggest a form of methodological pluralism in which multiple methods may
be necessary to increase our understanding of individual domains, and those methods
that are strongest in one domain, or on one problem, may not necessarily be
strongest, or even applicable, in other domains or to other problems. Yet this need
not, and should not, go anywhere near as far as Feyerabend’s epistemological
anarchy, with its denial of preeminence for any particular methods and its
notion that conventional science is just one among many ideologies [Feyerabend 1975]. All else being equal, the strongest among the
applicable methods should always be the most appropriate.
Domains can be ranked by the strength of the methods they are able to effectively
use, with the physical sciences traditionally able to use stronger methods than the
life sciences and the life sciences stronger methods than the social sciences. But
this should not be confused with a claim that this hierarchy correlates with the
quality of the science pursued within the domain. We have the need to understand all
of these domains, and good science is equally possible within each, based on the
strongest methods available for them. The methods used within the humanities,
although generally even weaker than those standard in the social sciences, can be
applied to increase our understanding within their domain, while stronger methods
have so far not proven so successful. They thus can potentially serve as the basis
for good science. Still, it is worth noting that even good science can be relatively
unproductive if the best available methods are insufficient to increase our
understanding of it to any significant extent.
The notion that the term “science” is appropriate for all human intellectual
endeavors that meet the criterion of tending to increase our understanding over time
can, to some extent, be viewed as a return to the original notion of philosophy, or
love of wisdom, from which modern science descended through the
splintering off of natural philosophy. But whether the generic is called philosophy,
or science, or even Wissenschaft — a German word for science that
includes not only those academic disciplines typically labeled as science in English
but also other areas of academic study, such as the humanities [Hansson 2008] — the key point is to consider how our understanding is
increased across the full range of subjects of interest, along with the methods best
able to increase this understanding.
The notion of a great scientific domain actually goes beyond even this broad notion
of understanding, to also include shaping. Understanding involves a flow
of influence from the domain of interest to a scientist, altering how the scientist
views the domain. Shaping is a creative activity that goes in the reverse direction,
with influence flowing from the scientist to the domain, resulting in alterations to
the domain itself. Shaping may more conventionally be thought of as engineering, but
traditional engineering only tends to focus on mathematically oriented shaping of the
physical domain. Many other traditional professional activities — such as law,
business, education, and medicine — are shaping activities as well, but in the social
or life domains. In computing, it can be very difficult to separate understanding
from shaping because most of what is to be understood has first been shaped, and in
fact created, by people. However, the same kinds of issues will become continually
more important in the future of the other domains as we are increasingly able to
create and modify physical, life, and social entities at their most basic levels.
Interestingly, the humanities are already very much like computing along this
dimension, in that they primarily study human-created artifacts. In Simon’s terms,
both computing and the humanities are “sciences of the
artificial”
[Simon 1969]; although even the distinction between natural and
artificial is unlikely to remain tenable in any fundamental sense as we increasingly
understand people as part of nature rather than as special beings outside of it, and
as our increasing power to shape all kinds of entities further blurs the lines
between what does and does not involve human intervention. With understanding and
shaping being two sides of the same coin, and with them being (increasingly)
intertwined across all scientific domains, I have been arguing that the top-level
decomposition in science should focus on divisions by subject matter into great
scientific domains, rather than on science (understanding) versus engineering
(shaping) or artificial versus natural. The latter distinctions can then be appealed
to as useful only via second-order within-domain organizational principles.
Broadly, a great scientific domain concerns the understanding and shaping of
the interactions among a coherent, distinctive, and extensive body of structures
and processes. Each such domain is then characterized by its distinctive
structures and processes. Structures are things of interest in a scientific domain,
while processes actively alter these structures over time. The physical sciences
focus on (non-living) matter and energy, and their associated forces. The life
sciences focus on living beings and the processes by which they live, die, and
reproduce. The social sciences focus on humans, their products, and their cognitive
and social processes. The computing sciences focus on information and its
transformation. In physics (physical sciences) we might, for example, talk about
particles and forces; in cell biology (life sciences), the focus may be on cells plus
how they originate, operate, and die; in cognitive psychology (social sciences), the
concern might be with the human mind and how it yields intelligent behavior; and in
compilers (computing science), the interest may be in programs and how they get
translated into executable form.
It is the dynamic richness and vitality of the interactions among an extensive body
of structures and processes that leads to great scientific domains. It is also what
drives the need for experimentation across much of the sciences. It isn’t that
science itself inherently requires experimentation, but that complex interactions
among structures and processes can severely limit the effectiveness of the analytical
methods that can be so useful in less dynamic domains. Mathematics, for example,
focuses almost exclusively on structures — equations, theorems, proofs, etc. — and is
thus able to make great strides without resorting to experiments. However, its
resulting lack of processes and their interactions with structures make it a
static domain that does not reach the level of a great scientific
domain on its own. Because mathematical structures are informational in nature —
rather than physical, biological, or social — it makes sense to consider it as part
of the great scientific domain of computing, with its broader focus on information
(structures) and its transformation (processes). According to this view, mathematics
is a part of theoretical computing that uses one of the strongest methods known —
proof — in understanding specific types of informational structures. The computing
domain as a whole broadens this to cover the understanding and shaping of the full
set of dynamic interactions possible among all kinds of informational structures and
transformational processes. Altogether, this domain comprises not just computer
science and mathematics, but also computer engineering, computational science,
informatics, and information theory, science, and technology.
The story for the humanities is analogous to that for mathematics. The humanities are
full of structures — books, paintings, statues, etc. — and analyses of such
structures, but there is, in general, little process to interact with these
structures. There are some limited exceptions to this, such as: disciplines like
history for which there is significant ambiguity as to whether they belong to the
humanities or the social sciences; and linguistics, whose informational aspect
implies an overlap with the domain of computing. However, aside from these
multidisciplinary outliers, the predominant lack of processes in the humanities
deprives it of the dynamic richness that demands experimentation and enables a great
scientific domain.
This essentially static essence of the humanities has been noted before, such as in
#janlert2000 where the artificial (shaped) nature of the
humanities is also discussed. The key additional point here is that, where there is
process in the humanities, it is principally human activity. When this is combined
with the fact that the artifacts studied by the humanities tend themselves to be
about people, it ought to be clear why it is natural to consider them as part of the
social sciences, when broadly construed as the great scientific domain that deals
with (non-biological) human structures and processes. The humanities become a mostly
static component of this domain focused on structures that help to reveal the
essential human condition, but span both the understanding of such structures and
their shaping (i.e., their creation). The close relationship between the humanities
and social sciences is already recognized implicitly in universities that combine the
two into colleges of humanities and social sciences, and in disciplines such as
history that are ambiguous about where they belong, but the suggestion here involves
an even tighter coupling, at least conceptually.
The political problem with such a merger is, of course, that stronger methods tend to
drive out weaker ones that strive to coexist in the same environment. Even in
subdomains where the stronger methods are not applicable, their presence in the same
intellectual environment can sap the credibility of the weaker ones. Computer science
grew out of mathematics in a number of universities, but had to separate itself, at
least in part, to have the freedom to perform experiments, a method that although
weaker than proof is essential in studying much of computing. The analytical and
critical methods of the humanities are weaker than those used in the more traditional
sciences, or even than those used in the rest of the social sciences, but they are
presumably particularly attuned to their subdomain of the social sciences, and can
thus still be valuable to the extent that they remain among the strongest methods
available for increasing our understanding of important aspects of people and their
culture. Even acknowledging this political problem, though, shouldn’t keep us from an
awareness of the true conceptual connection that exists between the humanities and
the social sciences, or of the understanding that this connection is possible without
diminishing either participant.
A Relational Analysis of the Digital Humanities
The relational architecture provides a means of analyzing scientific topics and
disciplines in terms of the great scientific domains they involve and the
relationships among these domains that are implicated. It also provides a vehicle for
systematically investigating the space of interdisciplinary overlaps that can occur
among domains. In this article, the focus is on analyzing the digital humanities in
terms of the potential space of overlaps the architecture identifies between
computing and the humanities. In computing, architectures often induce languages, and
the relational architecture is no exception. The Metascience Expression (ME)
language was developed to enable concise semi-formal representations of
complex, particularly multidisciplinary, scientific disciplines and topics, in
service of understanding them both individually and in aggregate. Expressions in ME
are provided in the remainder of this discussion in conjunction with explanations in
English.
At the top level of the relational architecture, the four great scientific domains
are denoted by their initial letters: P(hysical), L(ife), S(ocial), and C(omputing).
The discipline of digital humanities then concerns the relationships between two of
these domains: the social sciences (S) and the computing sciences (C). If the
addition symbol (+) is used to denote that there is some form of relationship between
two domains, we can express the digital humanities as S+C. However, we can also
introduce a new initial for the H(umanities) — with H understood to be a subdomain of
S (H ⊂ S) — to specialize the overall expression more particularly for the digital
humanities to H+C.
The relational architecture further partitions the generic notion of across-domain
relationships (+) into two general types: implementation (/) and
interaction (↔). Together, these two types of relationships have
proven adequate for understanding the multidisciplinary aspects of computing so far
investigated, and have even proven useful in illuminating many aspects of computing
not traditionally considered multidisciplinary.
An implementation relationship (/) exists between two domains when multiple
structures and processes in one domain combine to bring into being elementary
structures and processes in the other. The physical domain implements the life domain
(L/P) when molecules and their forces combine to yield cells and their processes.
Similarly the life domain implements the social domain (S/L) when neurons in the
brain combine with each other to implement thoughts in the mind, and the brain joins
with the rest of the body to yield human behavior. Sometimes this general form of
relationship yields a true or full implementation and at other times only a
simulation, where some definitional aspects of the implemented domain
are missing. For example, a computational simulation of a person — a virtual
human (S/C) — may look and behave much like a real person, but cannot
actually be one (at least as long as biological realization is part of the definition
of a person). In other cases, it may be hard to differentiate whether something is
real or simulated. Can, for example, the discipline of artificial intelligence
produce real intelligence without biological realization or can it merely yield a
computational simulation of intelligence? Disagreements continue over this question.
Still, whether reality actually results, or merely a simulation is produced, either
can be considered generically as an instance of implementation.
The implementation relationship yields multiple flavors of digital humanities. When
computing implements the humanities (H/C) we get digital cultural artifacts, such as
digital paintings, sculptures in virtual environments, immersive experiences, and
digital books. Given the dynamic nature of computing, we can expect an ever-larger
fraction of the future of H/C to involve active rather than static artifacts, whether
they are thought of as digital plays, videogames, or simply interactive experiences.
Sometimes H/C artifacts are digital reproductions (simulations) of existing
non-digital artifacts and at other times they are unique artifacts in their own
right. But even a reproduction may itself be a true cultural artifact; a copy of a
famous work of art may, for example, be a cultural artifact despite not being the
original it appears to be. In addition, all computing artifacts can themselves be
viewed as (implementing) cultural artifacts even if there was no such intention when
they were constructed. The area of critical code studies, for example,
views conventional computer programs as cultural artifacts, and applies the
humanities’ analytical methods to aid in deriving a more complete contextual
understanding of them [Marino 2006] The implementation of the
humanities by computing also yields computational linguistics, where computers
implement and simulate human language processing.
In the other direction, the largely static nature of the humanities means that it
cannot generally yield a full implementation of computing (C/H) — a book or a
painting simply cannot compute all by itself — although special classes of dynamic
cultural artifacts, such as complex mobiles, could conceivably be made to compute,
and thus to provide a full implementation. What a book or a painting can do is
provide a depiction or representation of a computer, essentially yielding a limited
form of static simulation. In addition, if we extend the notion of the digital
humanities from the overlap between computers (i.e., hardware and software) and the
humanities, to the overlap between computing (as a great scientific domain) and the
humanities, then the representation of information in general by cultural artifacts —
which is also denoted as C/H — could be absorbed within the digital humanities.
However, a broader appeal to the dynamics of the great scientific domain of the
social sciences — within which the humanities exist as a static subdomain — is
necessary more generally to fully implement computing. For example, a Wizard of Oz experiment involves a person acting as a
computer (C/S) in a situation in which a computer is either not available or would be
more trouble to program for the situation than it is worth. In such a circumstance,
the person is simulating a standard electronic computer — that is, a computer
implemented by the physical sciences (C/P) — but simultaneously socially implementing
an actual computer (C/S).
Interaction involves a peer relationship between two domains. For example, in human
computer interaction (S↔C), there is a bidirectional flow of information and
influence between entities from the social and computing domains. However, the
relationship can in general either be bidirectional, as in this example, or
unidirectional. Computational sensing, for example, involves flow of information from
the physical world to a computer (P→C), while robotic manufacturing involves flow of
influence from the computer to the physical world (P←C). In the digital humanities,
flow from the humanities to computing represents the automated computational analysis
of cultural artifacts (H→C); for example, determining clustering of authors based on
their literary styles [Luyckx, Daelemans and Vanhoutte 2006]. It could even be considered to
include recent work on machine reading, where computers automatically extract meaning
from text [Etzioni 2007]. In the reverse direction, a flow from
computing to the humanities represents computational composition (C→H). This is an
area still in its infancy, but that already includes, for example, computational
composition of simple poems [Manurung 2000], stories [Pérez y Pérez 2007] and drawings [McCorduck 1990]; and is likely
to eventually include novels, plays, movies and interactive experiences.
These two directions of interaction can loosely be considered as representing
computational understanding of the humanities (H→C) and computational shaping of the
humanities (C→H). In both cases, it is the computing domain that must be the active
partner in the interaction because of the static nature of the content of the
humanities. However, one way to remove this limitation is to shift the focus from the
static structures of the humanities to those active scholars and scientists who study
it. In the relational architecture, scientists are typically represented as members
of the social domain (i.e., people) who internally represent and simulate part of
their domain. For the humanities, this yields H/S. We can then represent the analysis
of computing artifacts by humanities scholars, as is for example studied in critical
code studies, by C→H/S. However, if the scientist is an expert in the combination of
the humanities and computing — denoted as (C↔H)/S — such studies should actually be
denoted as C→(C↔H)/S instead. Either way, this is a compound relationship involving
both implementation and interaction. It also includes the full social domain, to
represent the scientist, in addition to the humanities and computing.
Other complex variants of the digital humanities can also be represented in an
analogous manner. For example, human-computer collaboration in understanding the
humanities becomes H→H/(C↔S), signifying analysis of the humanities (H→) by a
human-computer entity (C↔S) with expertise in the humanities: H/(C↔S). Similarly,
more traditional forms of informatics within the humanities — where the computer
serves as a tool for use by the scientist rather than as a full scientific partner —
become H→(C↔H/S), where the humanities expertise is now limited to the human
participant. These relationships can also go in the reverse direction for shaping, or
be bidirectional to represent the interplay between understanding and shaping. But
either way, they involve two forms of interaction between computing and the
humanities: the interaction of computing with the humanities researcher and the
interaction of this pair with the humanities subject matter.
Linguistics provides an interesting special case. It is a core topic within the
humanities, and obviously has a tight coupling with sister disciplines such as
literature, but language is inherently informational and its use is predominantly
social. A natural expression for its subject matter would therefore be something like
C/S↔C/S — denoting informational interactions among people. If, as discussed earlier,
the purview of the digital humanities is expanded to include the full domain of
computing — not just computers themselves but information and its transformation —
then linguistics as a whole becomes a prime example of this broader notion of the
digital humanities; or at least of computational social science, since the expression
uses S rather than H.
For comparison, and to help evaluate the relational approach to understanding the
structure and scope of the digital humanities, it is informative to juxtapose it with
the five major modes of engagement between computing and the humanities discussed by
Svensson: “information technology as a tool, as a study object,
as an expressive medium, as an experimental laboratory and as an activist
venue”
[Svensson 2010]. We can ask both whether these five modes fit naturally
into the relational architecture and whether the architecture might indicate any
significant areas missing from the list (while acknowledging that the list was
unlikely to have been intended as comprehensive).
To start, it does turn out that all five modes fit naturally within the architecture,
although some yield more complex expressions than others. Computing as a tool used by
humanities researchers maps directly onto the informatics example above: H→(C↔H/S).
In this mode, the computer helps researchers acquire, manage, and analyze data about
cultural artifacts. Computing as an object of study implicitly views it as
(implementing) a cultural artifact: H/C. However, if we also want to explicitly
represent that such an artifact is being analyzed by a humanities researcher, this
expression can be extended to H/C→H/S. Computing as an expressive medium also takes
the form H/C because cultural artifacts are being implemented on computers, although
here we may want to expand this to H/C←H/S to emphasize the creative shaping aspect
that is central to this mode. The difference between the computer as an artifact and
the computer as a medium thus reduces to whether the focus is on understanding what
already exists as a cultural artifact — even if not initially intended as such an
artifact — versus deliberately creating new cultural artifacts. Computing as an
experimental laboratory relates back to computing as a tool, and thus to informatics:
H→(C↔H/S). However, instead of using computers as one-shot analysis tools,
interactive exploratory analysis is supported in this mode. In contrast with
traditional experimentation in active domains though, from what I can understand of
the description of this mode, it really does seem to be more exploratory analysis
than experimentation. Computing as an activist venue involves a shaping activity, but
here the shaping is of society at large rather than merely the scientific domain of
the humanities: S←(C↔H/S).
Based on the relational architecture, the two most obvious topics missing from the
list of five modes of engagement correspond to two simple relationships: C/H and C→H.
With respect to the first, the earlier discussion of the static nature of the
humanities implies that a full implementation of computing via the humanities is
impossible, except for the limited special case of dynamic artifacts. However,
cultural artifacts about computing — whether books, movies, or other forms — do fit
naturally here, as would all cultural artifacts embodying information if the digital
humanities were broadened to the full domain of computing. Any of these possibilities
yields a sixth mode of engagement based on implementing or representing information
and its transformation. The closest we have seen to C→H among the five modes
discussed by Svensson is H/C→H/S, where a humanities researcher studies fragments of
computing as cultural artifacts. The simpler expression can be considered as a more
abstract characterization of this kind of activity, with the focus narrowed to just
the relationship between the two primary domains involved. However, this simple
expression can also denote computing actively shaping the humanities, harking back to
automated composition — authoring, painting, etc. — by computers. As discussed
earlier, this is an area still in its infancy, yet it is one that could grow to
become a major component of the digital humanities.
As a final comment on the digital humanities it is worth noting that while Svensson’s
list is cast in terms of engagement with information technology — i.e., the more
applied tool-building aspect of computing — computing as a great scientific domain is
much more than just a set of tools. It is also theoretical results about information
and its transformation, algorithms for transforming information, and a wealth of
interdisciplinary topics involving interactions with one or more additional domains,
from artificial intelligence (S/C) and robotics (L/(P↔C)) to automated construction
(C→P), brain computer interfaces (L↔C), quantum and biological computers (C/P and
C/L), online social networks (S↔C)* — where the star (*) represents interactions
among arbitrary numbers of human-computer pairs — and the simulation, or possibly
even implementation, of everything (Δ/C, where Δ denotes all domains). The set of
possibilities opened up for the digital humanities by this broader perspective on
computing, and in particular by domain combinations that go beyond H and C to include
more complex relationships with additional domains, has yet to be tapped. We have
seen a few examples already where it has been useful to bring in the full social
domain (S), but it is not hard to conceive of further such topics, such as
collaborative human-computer composition: (S↔C)→H. Combination with the physical
domain leads to possibilities such as the computational analysis of both the content
and physical embodiment — i.e., implementation by the physical domain — of cultural
artifacts such as books, paintings and sculptures, yielding the expression H/P→C, or
H/P→S/C if the analysis occurs via artificial intelligence. The relational
architecture cannot all by itself identify where the interesting points are in this
larger space, but it does provide a systematic structure over the space, while also
guiding us towards an initial population of this structure.
Conclusion
The focus of this article has been on using the concept of a great scientific domain
to understand the humanities as a subdomain of the social sciences, without
diminishing either in the process, and then exploring the nature and structure of the
digital humanities via the space of possible multidisciplinary relationships afforded
by the relational architecture between the humanities and computing. The result is
hopefully a better understanding of both the humanities and computing, and in
particular of their overlap in the context of the digital humanities.
Works Cited
Austen 1818
Austen, Jane. Persuasion. London: John Murray, 1818.
Denning 2009
Denning, P.J., and P.S. Rosenbloom. “Computing: The fourth great domain of science”. Communications of the ACM 52 (2009), pp. 27-29.
Etzioni 2007
Etzioni, O., M. Banko and M.J. Cafarella. “Machine reading”. Presented at Proceedings of the AAAI Spring Symposium on Machine Reading, sponsored by (2007).
Feyerabend 1975
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