Abstract
Collaboration has become a hallmark of Digital Humanities (DH) research.
Nonetheless it remains under-discussed and for those not deeply engaged in DH a
bit of a mystery. Drawing on recent DH work and publications that engage with
questions of DH collaboration in different ways (e.g. [Deegan and McCarthy]
[Griffin and Hayler 2016]
[Hayler and Griffin 2016]), we analyse three types of DH collaboration:
1) human-human interactions; 2) human-machine/material interactions; and 3)
machine/material-machine/material interactions. We argue that engagement with
collaboration processes and practices enables us to think through how DH tools
and practices reinforce, resist, shape, and encode material realities which both
pre-exist, and are co-produced by them. We suggest that understanding these
entanglements facilitates a critical DH in which academic hierarchies and
disciplinary preconceptions are challenged.
Collaboration has become a hallmark of Digital Humanities (DH) research [
Nowviskie 2012], with scholars either actively engaging with various
parties across and outside of the university or becoming increasingly aware that
this is what they should be, or are at least expected to be, doing. In the European
survey on scholarly practices and digital needs in the arts and humanities [
Costis et al. 2017, 6], for example, seven out of ten Digital Humanities
researchers said they “often or very often” engage in
collaboration. Nonetheless, or perhaps because of this generalized
practice,collaboration has become increasingly taken for granted. However, we would
like to suggest that it remains under-developed in both theory and practice,
particularly with regard to partnerships that occur between academic and
non-academic (typically technical) collaborators and between human and non-human
actors (which, we argue, might productively be thought of as collaborators in the
co-production of certain resources and forms of knowledge). Collaboration in DH
research has not been ignored,
[1] but it is still under-discussed in the field,
particularly with regard to its diversity and subtlety. This can lead to particular
kinds of omission in the discourse around collaborative projects.
We were reminded of this during 2014-15 when we co-edited two volumes on
Research Methods for Digital Humanities – one on
“reading” digital data [
Griffin and Hayler 2016],
the other on “creating and curating” such data [
Hayler and Griffin 2016]. The aim of these volumes was to capture some
sense of the range of research methods that contribute to the Digital Humanities. We
therefore asked our contributors to discuss the research process of actual DH
projects they had undertaken, and expected that many of the chapters would include
some discussion of the collaboration processes integral to that work. But, to our
surprise, despite the fascinating accounts of DH research we received, we were
largely met with silence, and even active resistance, to this topic. When we tried
to elicit more sustained accounts of collaboration, one contributor emailed the
following:
The way I/we see it, DH is not a method. It is an
explorative field of research where we use digital tools and materials to
revisit and map out new ways in the motley field of humanities, with its many
different disciplines, often described as a big tent. We try to capture some of
this richness in our chapter by addressing central methodological issues we see
in different practices in[our disciplinary field]. Going into technical detail
in one project is not in our interest, and we do not think it will be useful for
the readers of your book to get a detailed technical account when entering a
field of constant technological development. What we focus on is rather to set
forth more longlasting opportunities and challenges. (Email communication to
Griffin and Hayler, 5/6/2015)[2]
We certainly have
some sympathy with the view that DH is not simply a method, or set of methods,
though some processes and practices have come to typify the field as it stands. The
contributor above reinterpreted our request for more detail on the nature of their
collaborations as meaning a ‘detailed technical account’ which they did not want to
provide. When we wrote the introduction to
Research Methods for
Creating and Curating Digital Data in the Digital Humanities we
therefore noted:
As all the contributions to this volume indicate, digitising
and curating digital data is a collaborative effort involving multiple
disciplines, skills and tools… [but] the engagement with technicians,
technology experts, and technologies is often left unaddressed...Indeed, it
can be surprisingly difficult to get experienced researchers to discuss this
dimension of their work.
[Hayler and Griffin 2016, 3]
How, then, might one promote a rich discussion of collaboration? A useful
approach can be found in Marilyn Deegan and Willard McCarthy’s collection
Collaborative Research in the Digital Humanities
[
Deegan and McCarthy]. The contributions to this volume provide an extremely
varied range of interpretations of what it means to
“collaborate”, that also offers at least a partial index of the
academic cultures within which such interpretations are made. We therefore take the
diversity of approaches of our own two volumes, alongside those in Deegan and
McCarty’s text, as our point of departure. In this article we do three things: we
begin with a brief typology of collaborations within the Digital Humanities and then
analyse the terms in which the types of collaborations we identify tend to be
discussed by DH scholars, emphasizing the present limits of the discourse whilst
also highlighting existing best practice. We conclude by exploring some reasons for,
and ways in which, more nuanced descriptions of collaboration become silenced, and
by re-emphasizing why such rich elaborations of collaborative research are vital for
scholarship and pedagogy in the Digital Humanities. Throughout, we suggest that
paying attention to the complexities of collaboration in DH research has the
potential to a) broaden the community base, b) strengthen its presence in
universities, c) demystify its processes, and d) further build an (at times
posthuman) understanding of how different actors, human and non-human, come together
in DH collaborations. This last point establishes what we might also more deeply
learn from studies of collaboration: knowledge is very rarely, if ever, produced by
individual human beings; coproduction is the typical experience and we politically,
pedagogically, and practically need a better language for articulating this reality.
Typologies of Collaboration
Collaboration, as a topic, is both normalized and widely discussed by many
fields. It is also a practice that is continuously engaged with, either directly
or indirectly, by all scholars, since their work a) dialogues with other work
and hence with other scholars, and b) near ubiquitously relies on computers for
searches, production, storage, and retrieval. In this section we therefore do
not claim uniqueness in terms of collaboration underpinning DH, but rather
address some of the features of collaboration that have specific salience for
DH. We highlight this because the image of the lone Humanities scholar,
frequently referenced in discussions of DH collaboration [
McCarthy 2005]
[
McCarthy 2012]
[
Rockwell 2012, 135], is receding as research funders
increasingly call for collaborative research projects,
[3] as universities in many countries continue to, or start
to, base scholars in research clusters, centres, or similar groupings, and as
the “impact” agenda of the Research Excellence Framework
(REF) in the UK
[4] demands engagement
[5] with
stakeholders and partners beyond the immediate academic context.
[6]
Collaboration has thus gradually come to be a demand, if not an explicit
necessity, for Humanities scholars, even as many types of research audit,
including the REF, consider their work predominantly in terms of individual and
individualized outputs. Such tensions offer one reason for the necessity of
addressing collaborations at this time, not least in fields such as Digital
Humanities where collaboration is considered, by many, to be a prerequisite.
Collaboration is a complex, intersectional activity in which multiple forms of
co-working often occur simultaneously, even as they are differentially
privileged, acknowledged or, as in some instances, not acknowledged at all. Such
practices of coworking might include:
- Various kinds of disciplinary, and cross-disciplinary, collaborations,
e.g. with colleagues in the same field, with colleagues from a different
field within the same knowledge domain, or with colleagues in a different
knowledge domain.
- Collaborations, as above, with people who are not colleagues, both inside
and outside of the university.
- Collaborations across different locations: co-locational, in different
places within the same culture, or in different places in diverse cultures
(academic, national, international etc.).
- Various intensities of collaboration: e.g. close-continuous;
close-intermittent; superficial-continuous; superficial-intermittent;
one-off or repeat; etc.
- Different kinds of collaboration between human and non-human actors (this
latter typically being machines (hardware) and/or processes (e.g.
software)).
Such collaborations may also be described in terms of specific dimensions such as
communication patterns. Clay Shirky [
Shirky 2003], for example,
suggests three types of communication patterns particularly associated with
online interactions and which might be used to identify types of collaboration:
- Point-to-point, two-way (as in a Skype call).
- One-to-many, outbound (as in newsletters).
- Many-to-many, two-way (web fora and other online group discussions).
To this list one might also add many-to-one, inbound (for instance in
crowdsourcing), and possibly others. Shirkey’s typology is based on human-human
interaction, where each set is defined by the number of contributing actants
involved and the direction of the most prominent flow of information. In
contrast, Timothy Butler and David Coleman [
Butler and Coleman 2003] put
forward five fundamental models of collaboration, defined instead by how
information and knowledge are generated:
- Library (a few people place material in a repository, many draw on it).
- Solicitation (a few people place requests, many respond e.g. a Request for
Proposal system, or crowdsourcing).
- Team (a small group working together on a project).
- Community (e.g. a Community of Practice).
- Process Support (systems that support repetitive workflows).
By focusing on the production, rather than just the spread of knowledge, Butler
and Coleman introduce the potential for explicitly including non-human actants
in models of collaboration.
Typologies, such as those above, are useful in elucidating specific aspects of
Digital Humanities work. They provide a theoretical, analytical framework for
thinking about collaboration rather than mapping its entangled realities. Here,
we would like to propose a different typology, designed to capture not just the
communication or generation of knowledge, but rather a meta-level focus on all
manner of interactions between actants in digital contexts. Such interactions,
as outlined, are direction neutral and allow for discussions of the travel of
information along any or all routes between collaborators. In this way,
interactions can be divided into:
- Human-human interactions
- Human-machine/material interactions
- Machine/material-machine/material interactions
Of these three types of interaction, the first and the second are discussed most
often within analyses of DH collaboration, though we would argue that their full
complexity is frequently overlooked. The third is, currently, the least
discussed, but requires further development in response to various new
approaches to materialism across the Humanities. Below we expand on each in
turn, focusing on the particular frame of academic DH research. It is worth
noting at the outset that this is an idealized framework; collaborations often,
maybe always, transgress the divisions shown above, with non-human actants
influencing human-human collaborations, which then reshape the non-human sphere
in turn. Collaboration, as we conceive of it, is truly entangled, developing
over time in ways which are complex to track. The proposed typology, therefore,
does not describe every collaboration, but rather elements that might appear in
every collaborative act.
Human-Human Interaction
Human-human interaction may refer to researchers and other partners who are
collaborating in producing and/or “farming” data in a joint
project, or to the connection between those who have produced Digital Humanities
resources and these resources’ end users (we see it as valuable to view such
interactions as collaborative precisely because they can result in the
co-production of knowledge). Collaboration between human actors is always
affected by the experiences and backgrounds from which such actors join the
collaboration
[7], and in academic work this is
typically most clearly seen in disciplinary divisions. In “Being the Other”, for example, Melissa Terras discusses the
relationship between Humanities scholars and their collaborators from Computing
or Engineering Science working on DH projects [
Terras 2012].
Terras argues that in such cross-disciplinary collaborations, “individuals...often find they are the
‘Other’”
[
Terras 2012, 213], i.e. they experience a profound sense of their own difference as their
work is defamiliarised – they are no longer “normal”. With
both sides destabilized in this manner, there comes “the need for the construction of roles and
responsibilities that allow their skill sets to be admitted to a working
team”
[
Terras 2012, 213]. The team thus often needs to be built from first principles, emphasizing
the skills that brought the collaborators together whilst minimizing the
friction of competing disciplinary norms (and, indeed, establishing new norms
that future collaborations may benefit from deploying). For anyone experienced
in interdisciplinary collaboration, there is little that is surprising here, and
yet such experiences of alterity, and its legitimate and frequent challenges,
are often forgotten or left unarticulated, rendering them as a surprise for each
new generation of scholars who might therefore reasonably feel that they, alone,
encounter such difficulties.
Terras’ notion of “otherness” has strong resonances with
feminist methodological work from the 1980s and 1990s where questions of
“what counts” (what knowledge, what skills, what
experience etc.) were key to debates about the status of women in the academy
and in knowledge production (see e.g. [
Harding 1987]
[
Hartsock 1998]). Underlying such questions of “what
counts” are (often deep-seated) inequalities, more or less
consciously acknowledged hierarchies of knowledge, method, and practice that
have led Willard McCarthy to suggest that “true collaboration within a group
happens rarely” because it requires an “un-boss”’,
someone, according to McCarthy, who is “
primus inter
pares”, i.e. an actor able to make calls on what is valid
and yet able to step back and allow others to lead and act as the work demands
[
McCarthy 2012, 2]. This is no easy task.
The very notion of a
primus inter pares indicates power differences,
and therefore issues of leadership and of equality which also deeply troubled
feminist research when the discipline of Women’s or Gender Studies was seeking
to establish and legitimize its work in academia (just as DH has been doing, in
its current form, since around the turn of the century). The early feminist
demands for the recognition of equality within knowledge production were very
much concerned with the inclusion of women in the academy (e.g. [
Gluck and Patai 1991]); the recognition of the co-production of knowledge
between researchers and those researched [
Oakley 1982], and an
attendant reduction in the assumptions of power differences between these; and
the inclusion and support of diverse research methods and processes. In a
similar fashion, most of the current debates in the Digital Humanities about
status, “being the other”, inclusion, legitimation, and
support revolve around status markers and power differentials between different
kinds of knowledge workers, and in particular between a) those who are
Humanities scholars and those who come from a technology or engineering
background, and b) academic and non-academic partners (there can be, and often
are, overlaps between a) and b)).
In “No Job for Techies”, for example, John Bradley
discusses how the construction of technical staff as “support
services” tends to figure these staff as occupying marginal or
secondary roles (the “secretaries” or services providers – in
feminist terms – of DH), and “the diminutive term
‘techie’” frequently reinforces that relegation [
Bradley 2012, 11]. Bradley addresses this tendency directly
in terms of power, arguing that in software development the need to involve the
external user in software design constitutes a model form of power sharing that
is often ignored [
Bradley 2012, 17]. Unsurprisingly, given
the emphasis on individual research publications in the UK, Bradley argues that
one way to change entrenched power structures within the Digital Humanities is
by “getting status for [technicians’ work] by framing it in
the context of doing research...[i.e.] reporting the results by writing
about them”
[
Bradley 2012, 18]. He himself reported directly on the 2008 Research Assessment Exercise
and on the “research outputs” that King’s College London
submitted, even though there was no separate panel for Digital Humanities (as
was also the case for Women’s and Gender Studies in their beginnings, and was
again the case in the most recent such exercise in 2014). The desirability of
submitting to such exercises in order to gain status is certainly up for debate,
but here we want to focus on the fact that Bradley’s discussion of power
differences among staff involved in DH projects explicitly recognizes status
differences and inequalities between academic and
“professional” staff (his terms) as one of the central
issues in DH collaboration. This may also work the opposite way, for instance
when technical staff are “the experts” that understand how to
“translate” a potential project into digital form, not
least as the requirement of complex digital work becomes the norm in research
projects, with masculinized tech workers dominating institutional discussions,
and commanding spending decisions, over a feminized academic class (not least as
these gendered differences often manifest as gender differences).
Bradley’s discussion of “alt-ac”
[
Croxall 2012] jobs constitutes another articulation on this
topic, showing how DH workers can become marginalized through the denigration of
certain kinds of expertise. Power differentials may manifest themselves in
myriad ways. Bradley’s fundamental point holds across them all however: power
structures both within and beyond the immediate interactions can lead to the
work of one or more collaborators being reduced or going uncredited, and to the
detriment of their institutional and subject standing.
It is important to realize that one way in which such differences can be
articulated is by remaining silent about their processes, thus side-lining, or
invisibilizing, certain categories of workers in DH or academe more broadly;
silence is also a form of articulation – it speaks. This is a point that we will
return to.
In different terms, Julia Flanders explores the power dynamics in human-human DH
research practices, suggesting that “collaboration...is always understood to carry with it
some kind of sacrifice, a trade-off between autonomy and synergy”
[
Flanders 2012, 67]. The vocabulary which characterizes her essay, from
“
sacrifice” to the “intensif[ication
of] the
concessions we make to the demands of the social
contract” and the voluntary
submission
“to norms of behavior and
constraints on
our freedom of action”
[
Flanders 2012, 67]
[8], further contribute to the sense of an
agonistic relation [
Mouffe 2016]. This is confirmed in Flanders’
assertion that DH is “a domain...where we see,
locked in
struggle, the drive towards absolute consistency and
technical processability on the one hand, and the drive towards critical
independence and disciplinary debate on the other”
[
Flanders 2012, 68]
[9]. Given this agonistic relation, Flanders
suggests that “our instinct may thus be...to treat disciplinary debate
as the opponent: something that needs to be eliminated or ignored in
order for collaboration to proceed”
[
Flanders 2012, 70]. But in this drive we might also (often) find the loss of exactly the
diversity across disciplines which makes collaborative work so desirable – the
same friction which produces hierarchical thinking, factions, and the need for
McCarty’s “primus inter pares” might also be marshaled,
instead, to produce the new thinking required to solve a problem or spot the
absences produced by particular knowledge domains’ norms and traditions. Such
solutions might well end up contributing to the very consistency and
processability that their necessary friction is so often positioned as
inhibiting. Flanders’ own response, to move beyond a survival-of-the-fittest
model of approaches to intellectual labour, is to try to define “the precise role of dissent within our collaborative
ecology”
[
Flanders 2012, 70]. We would endorse this approach. By increasing the understanding of what
each discipline offers to the collaboration
through rather than
despite its difference, otherness becomes a tool for
potentially overcoming technical, theoretical, and/or creative problems. Brian
Rosenblum and Arienne Dwyer report positively about their collaboration in
“Copiloting a Digital Humanities Center: A Critical
Reflection on a LibrariesAcademic Partnership”
[
Rosenblum and Dwyer 2016]. Although fully aware of the cultural differences
between the library as a service provider and being an academic as
research-oriented, they cite the differences between them as enabling them to “brainstorm creatively, problem-solve efficiently, fill
in each other’s disciplinary knowledge gaps, and alternate taking the
lead on any given activity”
[
Rosenblum and Dwyer 2016, 120].
Where Flanders looks at human-human DH collaboration in terms of a
quasi-evolutionary social politics, Geoffrey Rockwell discusses it as a relative
challenge, “not, I believe, to traditional individual research, but
to the organization of professional scholarship in general”
[
Rockwell 2012, 151]. Writing from a North American context, he distinguishes between
“the professoriate” and “amateur
researchers” (terms redolent of earlier notions such as the
“gentleman scholar”), proposing that “while we never believed that the professoriate should
have special legal privileges, we have acted as if we were a special
caste of intellectual worker who should be supported by society and
protected with administrative mechanisms like tenure”
[
Rockwell 2012, 151]. Such a statement, which also articulates power differentials of the same
order as those discussed above, seems somewhat surprising in 2017. In describing
the supposed challenge that will occur when “the distinction between the professoriate and amateur
researchers…blur[s] as more and more research is shared through social
media”
[
Rockwell 2012, 15], Rockwell inadvertently (we assume) re-asserts the power and status
differentials that he regards as being threatened. The reader feels transported
back in time when Rockwell claims that “crowdsourcing projects provide structured ways to
involve the growing numbers of well-educated amateurs with time on their
hands. . . Such projects . . . provide educated amateurs with a
meaningful way to use their leisure time”
[
Rockwell 2012, 151–2] – in other words, a form of “rational recreation” as
advocated in the 19th century to contain and control unruly workers and amateur
researchers. Far from recognizing a challenge to the standing of the
“professoriate,” this manner of speaking reproduces
certain conventions with regard to who is expected to have power and who is seen
to be any knowledge production’s final arbiter and owner. Rockwell, overall,
endorses co-production with the “crowd,” but established,
arboreal hierarchies remain the norm over more rhizomic understandings of the
generation of work. One might put a very simple question: if research could not
be produced without the crowd, does it make any sense to diminish their
contribution, or to see the academic as working in isolation and merely
facilitated by an instrumentalized general public, the crowd figured as a
neutral tool (i.e. something put to predictable ends with no effects of their
own)? We discuss the (im)possibility of neutral instrumentalization below in the
section on machine/material-machine/material interactions, but, as Gabriel
Wolfenstein’s chapter on crowdsourcing shows (see below), it is a mistake to
view the crowds that are sourced as un-invested or as purely, or neatly, a means
to a research end [
Wolfenstein 2016].
Thinking through another aspect of the ways in which power structures can be
written into co-production, Melissa Terras’ work on otherness in DH [
Terras 2012] is also amongst the most forthcoming in discussing
the vicissitudes of DH collaboration. As she writes: “I have encountered my fair share of disasters. Shall I
be honest? Things have gone horrendously wrong, often between
individuals who are supposed to be working closely together”
[
Terras 2012, 222]. Terras suggests that:
Most failures in projects...stem from a lack of
communication. Perceived slights of status or disputed ‘ownership’ of
published outcomes have ruined what promised to be an interesting and
fruitful working relationship. Those employed to do complex
computational tasks have not had the desired skill set after all,
meaning deliverables are not delivered.
[Terras 2012, 222]
What Terras brings out here is that power hierarchies not only shape
research, but that they are able to do so by pressuring collaborators into
promising too much, or blinding them to the complexity of projects that rely on
still-new methods and practices.
Terras is the only writer in Deegan and McCarty’s collection who explicitly names
the disasters that might occur in human-human interactions in DH research:
There was research which had to be abandoned after
months of statistical analysis because a party forgot to mention a
dataset that should have been included...There was the time when the
research assistant set out to sabotage a project because she did not
believe in a professor’s expertise, and the one about the research
assistant who suddenly left because the pressure was too much, taking
passwords with them.
[Terras 2012, 223]
In recounting these anecdotes, Terras shows a willingness to be honest
about the challenges and failures of human-human collaboration, the
documentation of which is both rare and yet absolutely necessary in order to
move collaborative research forward.
[10] The more that potential collaborators can learn from
previous work, the more likely their own chances of success, and this learning
cannot come from solely positive stories; the negative results of work slowed,
distorted, or abandoned may have just as much to tell collaborators as the
documentation of successful projects. Hence our desire, expressed above, for our
own collections on research methods to include the realities of the work
undertaken.
Terras’ litany of what can go wrong can seem depressing, and her assertion that
“The biggest issue…with many ambitious digital humanities projects...is
the lack of identifiable outcomes at the close of a project which were
promised at their start” is, in some respects, deeply worrying [
Terras 2012, 223]. But, whilst we did not find the lack of
identifiable outcomes that Terras describes in the volume we edited on
Research Methods for Creating and Curating Data in the Digital
Humanities
[
Hayler and Griffin 2016], that volume’s contributions did also
indicate that not everything always went to plan. Mats Deutschmann, Anders
Steinvall, and Anna Lagerström’s chapter on “Raising
Language Awareness Using Digital Media: Methods for Revealing Linguistic
Stereotyping”, for example, details how voice manipulation through
voice morphing software (intended to manipulate the way that participants read
subjects’ gender) always sounded artificial and thus made it difficult to know
if participants’ responses to the voices that they heard were due to the
particular characteristics of a voice, such as sounding
“female/feminine”, or to the
“unnaturalness” or artificiality of the voice’s tone [
Deutschmann et al. 2016]. This is an important finding, not only because
it points to a key issue in technological embodiment, namely how to make the
non-human seem convincingly human, but it also highlights that a significant
proportion of DH research is currently exploratory in nature, both in its
research questions, but also in its emerging methodologies and technical
requirements. Hence Deutschmann et al’s findings cannot be described as
“negative” or as leaving them with “nothing to
show for their efforts”. Rather, they offer (in a similar way to
Terras) a particular result in a chain of work that may eventually lead to
better voice morphing technology and hence to better ways of teaching people
about the effects of the particularities of voice on the hearer.
Where discussions of interactions in DH research between human colleagues reveal
some of the tensions and power struggles between peers from diverse disciplinary
backgrounds, such struggles can be less immediately obvious when collaborations
are more remote, as in the context of crowd-sourcing, or when producing DH tools
or databases for end users who may remain completely unknown to these products’
producers. Like a number of contributors to
Collaborative
Research in the Digital Humanities, Susan Hockey (2012), in “Digital Humanities in the Age of the Internet: Reaching Out to
Other Communities”, takes a research assessment-driven view of these
“outreach activities”, where reaching out to
“wider society” and “developing links with a broader range of
organisations”
[
Hockey 2012, 90] is construed as a means of academic survival. Highlighting the expense of
large Digital Humanities projects, Hockey uses the phrase “indirect
collaboration” to describe the “re-usability”
of data and tools produced in the academy that find application and use outside
of that space, although she also argues that “the humanities computing community has not been
particularly good at promoting its activities beyond academia”
[
Hockey 2012, 90].
There is a way in which, in the neoliberal terms that Hockey’s chapter itself
uses, what she writes about are “derivatives”: asynchronous
uses of data and tools by a “wider”, hence unknown, public.
This form of collaboration is cast as a supply-and-demand structure where
academics act as producers and “wider society” (amorphously
construed) as consumers. There is no immediate interaction except via the
machine, i.e. this collaborative mode pushes at the boundaries of human-human
contact, though we would argue that such contact is mediated by the
secondary and simultaneous human-machine/material interaction rather than wholly
replaced by it; in terms of the use of tools to produce knowledge, there is a
useful sense in which the producers of the tool and the end user co-produce the
final output, with the producers’ share of the input being hugely variable
across different projects and tools. This also further reveals that our typology
of interactions does not describe research activity in mutually exclusive terms
– interactions between actants nearly always overlap and coshape one another,
for example where academic transdisciplinary partnerships intersect with
interactions with technicians recruited to facilitate such new collegiate
research and the equipment that they bring to such tasks.
Somewhere between the intimacies of collegial collaboration that are most often
discussed and the distanced collaborations described by Hockey sits
crowdsourcing. As Gabriel Wolfenstein has argued, “knowing your
crowd” is key; here collaboration is about the give and take
between researchers and researched, or knowledge owners [
Wolfenstein 2016]. Wolfenstein offers an illuminating account of
how the project
Living with the Railroads[11] required sustained engagement with the rail
fan community, guaranteed well beyond the lifetime of the project, in order to
gain access to the materials held in the community. This investment was achieved
by inviting several rail fans onto the Crowdsourcing Trains Advisory Board.
These members, and the wider fan community, were then encouraged to act both as
sources for core material for the project and as end users. This virtuous circle
was both enabling and productive, but it required long-term commitment outside
of the explicit project parameters. One might wonder to what extent there is an
appetite for this kind of commitment amongst academics who may not regard
crowd-sourcing as the collaborative co-production of research, but rather as the
potential to draw on existing communities as an instrumentalized resource, i.e.
as another tool for getting research done and nothing more.
In considering discussions of human-human interaction in DH research
collaboration, it is noteworthy that concerns frequently relate to status,
primarily the relative status of collaborators in terms of pre-existing
disciplinary and functional hierarchies, the standing of those people within
their research communities and collaborations, and the perceptions of such from
outside. For UK academics, collaboration is often filtered through the lens of
the REF which, in many respects, does not favour collaboration. In this
environment, technicians and non-academic partners can be constructed as
“second-class citizens”, the feminized servants of the
collaboration process who simply facilitate the “real”
academic work. But such attitudes may well contribute to the kinds of failures
that Terras describes and are, if nothing else, inaccurate.
Collaboration is fraught, achieved against and despite odds [
Griffin et al. 2013]. Such odds include, inter alia, the reliability of
those with whom one works. The realities of skillsets requirements are also
always coupled with collaborators’ views of their and their co-researchers’
social standing and relative merits, and all of this is further intensified by
the realities of a neoliberal research landscape (which might be thought of as a
prickly and jealous collaborator in its own right, such is the profundity of its
effect on final outputs) and the still-present support of colleagues and
co-conspirators. Such factors, positive and negative, could be expected to be
less evident in the human/material-machine collaborations to which we now turn,
but non-human actants, as we shall see, are also never neutral – they bring
their own frictions.
Human/material-machine collaboration
Since the late 1970s, we have seen the gradual emergence of new materialisms,
developed from (most explicitly) Bruno Latour’s and Steve Woolgar’s
Laboratory Life
[
Latour and Woolgar 1979], Latour’s
Science in
Action
[
Latour 1987], Donna Haraway’s
Simians,
Cyborgs and Women
[
Haraway 1991], and Andrew Pickering’s
The
Mangle of Practice
[
Pickering 1995].
[12] The questions asked by Latour’s
networks of human and non-human actants, Pickering’s mangle of humans and their
tools, Haraway’s cyborgs, Jane Bennett’s entangled vibrant matter [
Bennett 2010], and what Karen Barad terms the
“intra-action” of humans and matter have dramatically
changed perceptions of materiality and nonhuman agency in human affairs [
Barad 2007]. There is now an increasing recognition of how
materiality shapes and circumscribes human action in entangled processes and/or
complex systems, rather than human-machine/material intra-action being a one-way
street with humans exclusively setting the direction. With work in DH exploring
tools alongside texts, we argue that an understanding of machines as
collaborators in knowledge production, and an awareness of the impacts of
materiality on such production, becomes a disciplinary as well as philosophical
concern. Materials and machines materialize; new materialisms conceive of matter
as exhibiting agency [
Coole and Frost 2010, 7] and resonate with
posthuman philosophies aimed at moving an understanding of human action from
impositions of human will on inert stuff to contexualized and contingent
processes in intra-action with lively matter. Such recognition challenges any
understanding of collaboration as a solely human activity; it demands that we
conceive of the agency of the material/machine, a fact to which DH scholars pay
some, but still limited, attention. This is evident, for instance, in Cecilia
Lindhé et al.’s chapter for us on “Curating Mary
Digitally”, concerned with the question of how medieval
representations of Mary might be effectively remediated digitally [
Lindhé et al. 2016]. Lindhé et al. accept N. Katherine Hayles’, and
others’, assertion that “the remediation and organization of knowledge shapes
our thoughts and actions”
[
Lindhé et al. 2016, 142], and for that reason the team wanted to move away from a simply mimetic
reconstruction of representations of Mary. Instead they decided to “direct attention to physical interaction and to the
materiality of the work”
[
Lindhé et al. 2016, 147], substituting mimesis with the creation of digital interfaces that
reacted to their viewers’ movements. Whilst this approach shifts attention from
the work as product to the work as a text produced in the process of viewing, it
does not inherently move away from attributing the primary or dominant agency to
the human. The reproductions are seemingly given some active role, but it is the
viewers’ movements that determine what is seen; the performance aspect of the
exhibition grants the material a secondary agency that remains subsidiary to the
human viewer. Lindhé et al. offer a great model of the kind of approach that can
lead to matter being made more obviously vibrant in the digital display of
artworks, but in this work the object’s most visible agency remains parasitic
off human input. Such an approach was important for the team since they
explicitly wanted to privilege the aesthetic and humanistic over the
technological, following Johanna Drucker’s injunction not to be overwhelmed by
computational methods, or to grant them primacy [
Drucker 2009].
Lindhé et al.’s work shows how digitization can be deployed in producing new and
conscious efforts to reframe materiality, but it also signals the continued
struggle over how the human and the material might be integrated, or conceived
of as integrated, without having to hierarchize their roles. In any interaction
there may always be a dominant actant (or actants); in Latour’s terms, they are
equally actors even if they do not act equally. The challenge comes in
materializing the real agency of actants which are not human such that we might
better realize the complex roles that they can play in the co-production of
knowledge and experience.
Non-human agency has increasingly made its way into Digital Humanities work
concerned with the effects, typically on reading, of the materiality of media.
The work of Hayles ([
Hayles 2002]
[
Hayles 2012]), Kirschenbaum [
Kirschenbaum 2008],
and Hansen [
Hansen 2006] has been hugely influential in this
regard, setting the terms for why a richer understanding of materiality might be
important for DH. For our purposes here, such understanding may refer to the
collaborations between the producer and the tools of production, the producer
and the digital product, or between the product and its user.
Matt Hayler, for example, uses the resistance to the new materiality of ereading
devices, such as the Kindle and the iPad, to underpin a
postphenomenological
[13]
philosophy of technical practice in his
Challenging the
Phenomena of Technology
[
Hayler 2016a]. In our collection on reading digital data, Hayler
explores the particular relevance of materiality to DH research into reading,
from hyperlinks producing specific kinds of reading practices on screen to the
distinctive physicalities and entailed effects of paper and electronic page
spaces [
Hayler 2016b]. In the case of hyperlinks, the digital text
acts on the user, inviting her to engage physically by clicking and scrolling,
and mentally by encouraging rhizomic or networked, as opposed to linear,
readings. Hyperlinks challenge the default grammars of the book-bound context
and promote novel forms of engagement that co-shape the ways in which the human
reader responds to the text – as Hayler states, hyperlinks are reminders of the
possibilities for connection inherent in every written word.
In discussing the raw materiality of the printed or pixelated page, however,
Hayler joins Hayles, Kirschenbaum, and Hayles in assessing how the materiality
of each medium can significantly structure the reading experience without
requiring a more obvious human interaction such as the following of a link.
Hayler shows how media, far from being parasitic on human agency, in fact
structure the agency of their users, altering their sense of their potentials.
In identifying the histories of the use of pages, with their inherent tendency
toward promoting e.g. linearity and fixity, and the newly forged histories of
screens, with their tendency towards promoting networked and transient
information, Hayler shows how materiality might become bound up with the content
of the writing itself in producing meaning, something which feels particularly
weighted at a moment of transition between two significant forms. By exploring
such entanglements of matter and meaning we can better understand how all texts
are co-shaped by a reader, a script, a medium, and a context, each of which
possesses its own meaning-making histories. Whilst reading remains a human-led
pursuit, there is a posthuman reshuffling of agency here, removing the human
from the centre and re-invisioning the reader as a co-constructor of meaning.
Hayler ends by suggesting that “digital technology is becoming humanized: made subtle,
not jarring, truly deep, not ghostly or shallow, and
meaning-rich”
[
Hayler 2016b, 31]. Humanization is here conceived of as reducing difference (which echoes
the reduction of otherness between academics in different fields, or between
academics and technicians (or crowds), in the human-human interactions discussed
above), the recognition of sameness, not just in the comparison of digital text
with print’s cultural caché, but also compared to the extent and depth of its
material agency and its parity with the human in being an actant in the
entanglement that produces the text to be read. That issue of material devices
as legitimate and agential actants is central to a full understanding of
human-material/machine collaboration.
In “The Object and the Event: Time-Based Digital Simulation
and Illusion in the Fine Arts”, Stephen Hilyard discusses the
creation of simulations through digital tools [
Hilyard 2016]. This
chapter analyses two distinct forms of collaboration between human and non-human
actants – that between an artist and her digital tools, and that between a
digital object and its viewer. The former collaboration, however, is couched in
terms of subservience (of the tools to the maker), and the latter reveals the
digital object to possess agency only in relation to its being viewed, creating
a psychological and/or physiological reaction that the viewer cannot escape.
As traditionally conceived, the artist, as authority and meaning-maker, remains
both in control of her tools and her deployment of them in the manipulation of
the viewer. The idea of the tool as an object that the user deploys in order to
achieve a particular end is a conception of intrinsic subsidiarity; in the
manufacturing of the artistic simulations that Hilyard describes, hardware and
software provide a service. Hilyard explores such manipulations in the context
of the realistic simulation of impossible effects, from the “uncanny
valley” of images of close-to-but-not-quite real humans to the
visually accurate rendering of things that could never exist or act as they do
outside of the simulation, things that trick the viewer with a blend of
photorealism and the subversion of physics. This sets up a classic fine art
paradigm where authority accrues to the artist as maker: the digital tools are
just that, and in being seen as subservient to the user, as instrumentalized
matter, their own agency is neglected. Similarly, the viewer is seen as being
worked upon by the art work rather than entangled with it in the co-production
of meaning, an interesting inversion of the hierarchy of human and object as
opposed to its flattening – to understand objects as entangled, we argue, is not
simply to reverse extant understandings (such that humans become slaves to their
artefacts sometimes), but instead to appreciate how agency might always flow
back and forth, with humans impacting on technologies and the ways in which they
might be deployed, and technologies impacting on their users such that the
outcome of use is not determined by either side.
In this vein, throughout the work of the new materialists introduced at the start
of this section, we find a much more sustained recognition of the role of tools,
artefacts, and objects more broadly in both human affairs and wider networks
(where humans might not even be involved in some collusions, as we shall discuss
in the next section). The post-phenomenologist Don Ihde, for example, offers a
taxonomy of technological interactions: embodiment relationships (where the
artefact melts away, such as using a hammer or tennis racquet); hermeneutic
relationships (where the tool both enables and modifies perceptions as it is
perceived through, such as using a microscope, telescope, or machine readout);
alterity relationships (where the machine is an explicit object of concentration
apart from the user, such as using an ATM machine); and background relationships
(where the device is set and then modifies the background of experience without
further interaction, such as the use of a thermostat and household
heating).
[14] In Ihde’s breakdown of engagements,
we start to see the myriad ways in which technologies both explicitly and subtly
flavor and condition experience, becoming a part of our perceptions and, in some
instances, enabling them to happen at all. Edwin Hutchin’s work on
“distributed cognition” (e.g. [
Hutchins 1996]) similarly demonstrates the extent to which tools might enable the conception
of action, as well as facilitating it, and even further displaces the human from
the centre of action. His description of navigation on board large naval ships,
and the tasks undertaken by combinations of personnel and equipment,
combinations that elide neat boundaries between human and non-human actors, sees
the network of actants as a distributed system that can legitimately be seen as
cognizing; it is the
system which thinks, and only the system which
can have these kinds of thoughts. In the absence of the non-human actors nothing
could be done, and certainly not done like this; no one human has control and
everyone and everything plays their part toward the goal.
When it comes to DH research, our interest here is in how systems of humans and
non-humans co-produce knowledge. What we take from the work of Ihde and
Hutchins, and the science studies of Latour, Pickering, Haraway, and Baird, are
that tools are never neutral. They, like technicians and crowds, are more
rightly thought of as collaborators, whether they are conceived of as such by
their users or not. Latour, Pickering, and Hutchins, in particular, are
sensitive to the ways in which tools’ users, especially in technical and
scientific contexts, are typically loathe to see the machines with which they
work as anything but neutral. As with human-human collaborations, much is
invested in the tools not interfering with the results and in the production of
knowledge being ascribed to the individual (and “excellent”
researcher), not to anyone/anything she might have depended upon. A profound
silence can emerge from hoping to avoid the comment sometimes offered to
photographers: “That’s such a beautiful picture; you must have a really great
camera!”
Non-human agency and the presumed neutrality of digital research tools are
explored in Thomas Nygren et al.’s “Connecting with the
Past: Opportunities and Challenges in Digital History”
[
Nygren et al. 2016]. Here the focus is strongly on the limitations of
digital renditions of information, couched in the following terms: “assumptions about the unproblematic application of
tools to data can be problematic, not least because there is a risk that
“data” will be shaped by and for the logic of digital tools”
[
Nygren et al. 2016, 63]. In this regard, Nygren et al. recognize the ways in which data, as read
or received, can be altered by or for the tools used to perform the analysis.
The tool introduces its own effects in its parsing of data; in short, as Ihde or
Pickering, or Latour also make clear, we make a profound mistake if we see such
tools as simply reporting or neutrally working with “the”
data
[15]; data are produced for and interpreted by non-human actors as
much as for and by human collaborators.
Nygren et al. countered the limitations of their tools, when more adequately
recognized, by augmenting the digital with the stories that the flesh and blood
researchers could tell, derived from a combination of research methods. By
better appreciating what their tools could and could not do, and why and how
their specificity might distort seemingly inert or neutral data sets, they were
able to see what gaps could be filled in the story with different approaches
that didn’t rely on digital computation. As they state: “researchers use a mixture of traditional and digital
methods to better understand the life and circumstances facing people in
the past. Visits to the archives have been combined with visits to the
places of historical events. In the practice of digital history, this
connection to historical settings is still important”
[
Nygren et al. 2016, 80]. They acknowledge that “materiality may certainly affect our construction of
knowledge”
[
Nygren et al. 2016, 80], but find this much easier to cope with in relation to conventional
historical methods (such as visiting actual sites) than in relation to digital
constructions or the unfamiliar whose logic is resisted. The collaborators,
human or non-human, that are best known can be mostly easily dealt with –
recognizing digital tools’ effects as, without deep investigation, unknowable
might be a significant aspect of successful collaboration with them.
In much of the analysis of human-material/machine collaboration specifically
within DH, insofar as it occurs, debates about how we became posthuman [
Hayles 1999], or our relative posthumanism as researchers or end
users, remain very much alive in the sense that the idea of collaboration with
non-human actants tends to be evaded, resisted, or not fully engaged with.
Actual making processes are frequently glossed over, the process of the
materialization of artifacts, and their agency, is elided, and the liberal
subject of the sovereign maker, or viewer, tends to remain firmly in place. But
what happens when that subject retreats entirely?
Material/machine-material/machine collaboration
In purely material interactions between non-human actants we might expect the
power hierarchies and politics of human-human and human-material/machine
collaborations to be absent as the human retreats from the scene. But for the
overwhelming majority of instances in DH research,
material/machine-material/machine collaboration requires at least prior
human-material/machine interaction (and likely prior humanhuman partnerships) –
in this way, material-material collaborations rest upon, or are simultaneous
with and shaped by, the other types of interaction that we have discussed.
Matter, of course, interacts without human involvement all the time, but in
digital research it is only in the rare cases where hardware is programmed to
roam free, or algorithms are left to produce emergent effects without
intervention, that we might consider humans as removed from digital processes,
and even then humans are necessarily the originators of the device or algorithm
(or of the hypothetical machine which might create or code them). As Julia
Flanders states, technical and professional standards in digital research are “an extraordinarily effective mechanism for
representing...an agreement...between collaborating
parties”
[
Flanders 2012, 79]
[16]. Standardization also enables
material/machine-material/machine collaboration, i.e. standards allow systems to
read, or “talk” to one another about data or processes, and
one question then becomes the extent to which a user or technician ever deals
with an open system. Or, put another way, what room is there for the unexpected
and unintended, the non-humanly conceived, to emerge?
Laszlo Hunyadi points out that “human-human interaction seems to be hard to represent
in regular form”
[
Hunyadi 2012, 101] since our understanding of human communication as a pattern is limited –
alternatively, it is in their seemingly obvious regularity that interactions
which contain non-human elements are presroumed to be more easily imagined.
Hunyadi’s university in Debrecen has created a cognitive robotics lab into which
a system of secondary labs without robots is to be networked in order to allow
interaction and experimentation between them as a system (
www.virca.hu). The project website
states: “Imagine a completely new form of relationship with your IT devices: a
relationship in which the mouse and the keyboard seem like ancient relics
used only by geeks and IT professionals; a relationship in which your
“desktop” is much more closely linked with your physical reality, and in
which social interactions play a central and practical role.”
[17] The derisive conflating of “geeks and
IT professionals” aside (another example of the power hierarchies
described above), this work is concerned with reducing the boundaries between
human and machine, and with creating a cybernetic homosocial triangle [
Kosofsky Sedgwick 1985] between human operators and the virtual
spaces in which they remotely collaborate on projects, their object of exchange.
The video showcases available online, which depict the virtual lab in Debrecen,
are all shot from the perspective of the human in his computer room or lab
space, looking at the screen. In one video, a robot in the actual room,
child-sized relative to the adult male manipulating it, is walking, imitating
the adult’s steps whilst the robot is also simultaneously projected into the
virtual space. The overall impression is of the scientist conducting an
experiment, ruling over neutral and inert matter, and controlling virtual space,
even if remotely. The primacy of the human remains clearly in view here, both
actually and conceptually – what seems like a hi-tech entanglement remains the
same story of human dominion.
Such primacy is hardly inevitable, however, nor does it guarantee the
hierarchized relation between human and material/machine. Outside of the DH
context, we have already seen quite a few signs of this not being the case, of
the unintended consequences of interactions, such as traders bringing down
markets in a few misclicks, or the biases that are daily being coded into data
sets and search algorithms via normal (though silently discriminatory)
use,
[18] etc. To
date, machines still need humans, at least for their design and/or the
initiation of the processes that they enter into. However, the idea that this
produces “control” (by humans) is erroneous – there can be
unintended consequences, such as collapsing markets and biased data sets, which
themselves generate important questions, much discussed in the context of
artificial intelligence and science fiction, about the desirable relation
between human and digital – are we looking at relations of subordination or of
“companion species”, of symbiotes, parasites, or some
other, as yet unrealised entanglement?
Deepmind’s AlphaGo Zero AI is one system provoking significant discussion in this
area. AlphaGo
[19] was originally created and trained to beat human
grandmasters of the game Go, a territory-claiming game that, unlike chess and
checkers, computers have struggled to compete at, even with only moderately
above-average players, due to the human “feel” required.
There are so many possible permutations of the board that the “brute
force” strategies that AIs have tended to draw on in simpler games
(trying out as many hypothetical moves as possible before settling upon the best
one and playing it in each situation) are simply too time- and
computation-hungry to be viable. AlphaGo instead used a combination of human
programming, brute force searches, and machine learning – it played a huge
number of games against both human and computer opponents in order to train
strategies for as many board permutations as possible.
When the original AlphaGo system did beat Lee Sedol, one of the world’s best
players, in 2016,
New Scientist reported on the
win’s intensifying of an “artificial intelligence phobia” in
South Korea [
Zastrow 2016]. The presumption of the humanity
required to undertake successful Go playing caused a crisis; the computer was
assumed to have equalled the leading player in some ineffable category outside
of mastery of the game. But AlphaGo was still heavily dependent on human
interaction – it was programmed by humans, played against human players in
training so as to learn from them, and was explicitly aiming at human-like
performance in its first incarnations, having millions of moves from expert
players coded into its database of possible solutions as its model of what
success should look like.
AlphaGo, however, has changed. AlphaGo Zero (AGZ) recently beat the original
AlphaGo system, and, significantly, had far less human-interaction in its path
to becoming the greatest Go player in the world. By again deploying machine
learning, AGZ similarly played a vast amount of training games, storing
knowledge of the best strategies from each one. But it never played a human
opponent, and it was never coded with human strategies or any human’s expert
moves. It began with completely random play against an iteration of itself –
whichever version won, by chance, would go on to play against a new iteration of
itself, with the now ever-increasingly competent versions progressing in an
evolutionary fashion. The system is now hugely more efficient, and, in the words
of its “designers” (probably now the wrong term), “it is no longer constrained by the limits of human
knowledge”
[
Hassabis and Silver 2017]. AlphaGo Zero became more competent than the version of AlphaGo that beat
Sedol within three days, and better than the best iteration of the original
within 40. The team never found out how good AlphaGo Zero could get; with the
wider actants in the system affecting things, as always, they “needed the computers for something else”
[
Etherington 2017].
When it comes to the production of knowledge about the game Go, AGZ leaves humans
in its wake, and it demonstrates the potential for uncoupling humans from
computational systems once they have been set up – humans, like their tools, are
not neutral: their biases and limitations can flavour a whole system. AGZ’s
collaborators in knowledge production, then, straddle the human-machine and
machine-machine. It is, and continues to be, its own collaborator, a
billion-fold, shaping itself in an entanglement that goes both ways: as each
iteration progresses it might lose out to the next incarnation of itself, ceding
the ascription of knowledge to the next version that it decides is better. As
colleagues and equipment join and leave knowledge-producing systems in more
conventional research, so AGZ recruits and rejects itself, over and over. When
institutional politics, funding limitations, labour laws, and good graces are
replaced by ruthless evolutionary mechanisms, vast progress can be made, and
this makes something very clear: we do not want the politics of machine-machine
collaborations to transcend their sphere. As the examples of machine-learning
algorithms encoding racists and sexist attitudes attest
[20], however, politics does tend to travel –
humans may not look like an explicit component in the collaborative system, but
their entanglement gets revealed in their shaping effect, as with any other
actant. Digital researchers need to function as stewards alive to the effects of
all actants, not presume neutrality, as they increasingly pursue machine-machine
collaborations which might take on the worst of us or, reversing the flow, to
resist the machine’s unsentimental excisions of one another becoming a model of
best practice.
Material/machine-machine/material interactions are also becoming more complex as
the ways in, and means by which combinations of hardware and software must speak
to one another proliferate. While some materials, such as the fore-edge
paintings on the compressed pages of books in the private collections of
affluent 19th century readers, may still resist being digitized [
Trettien 2011], as different kinds of materiality encounter each
other, those differences generate new and diverse interactional relations that
pit algorithmic logics against noise both from within and from without. In an
article on instance-based algorithmic learning, Aha et al. poetically describe
their “extended algorithm's performance” as “degrade[ing] gracefully with increasing noise levels
and compar[ing] favourably with a noise-tolerant decision tree
algorithm”
[
Aha et al. 1991, 37]. Noise or interference is expressive of difference, and it is such
difference that material/machine interactions must increasingly struggle with as
we attempt to recruit them in archiving and interpreting the world. Such
struggles contribute, we argue, to the reasons to be silent around collaborative
efforts in DH: difficulty in comprehension and expression are the reasons that
many phenomena go unarticulated, but the various silencings of diverse forms of
collaboration in DH research also have other sources to which we now turn.
Reasons To Be Silent
Discussions of crowd sourcing, or the example of Gabriel Wolfenstein’s depiction
of how the gathering of material was facilitated through engagement with
enthusiasts in the
Living with the Railroads
project [
Wolfenstein 2016], can offer useful advice for DH
scholars about how to access certain communities who might be described as
potentially “hard-to-reach,” and also how to conduct such
research, i.e. what new methods and practices might be required. But, as
suggested earlier, such concrete engagement with questions of collaboration is
not always forthcoming; we want to suggest four core reasons why this might be
the case which relate to the key points outlined in the typology of interactions
above.
1. Status and Speed
The first concerns the status and the role of the expert in research. Richard
Sennett has produced an important account of
The
Craftsman and the ways in which notions of craft have undergone
changes over time [
Sennett 2008]. Sennett identifies how, on
the one hand, many skills-based activities can now be executed by
“unskilled” persons (or, as above, removing people
altogether) because technology significantly facilitates or replaces
production processes, and, on the other, craft as an expression of honed
skill, socialized and shaped by practice and experience
over
time, is bulldozed through by the demand for the ever-increasing
acceleration of work processes (we can see this clearly when various forms
of “slowness” in production are conceived of as radical
as opposed to, say, appropriate). Instantaneity has become the name of the
game, from the production of goods to the production of research, and that
makes the expert who can provide patient and detailed explanations a
seemingly redundant figure (the expert who can be swift and reactive
however…). Silence regarding technical ability thus tends to sideline those
with specific expertise, particularly where this involves complex skills,
reinforcing a power hierarchy that sees academic experts as producers of
knowledge and technicians as instrumentalized supporters of this production.
This view oversimplifies skills acquisition and maintenance for both
academics and technicians, and does not recognise the actual practices of
collaboration as they consistently manifest.
2. The Pace of Change
The second reason why concrete engagement with collaborations may not be
forthcoming is also related to the issue of technology as such: as the
writer of the email quoted at the outset of this article suggests,
“constant technological development”, and by
implication the obsolescence that accompanies contemporary digital
technology, makes discussions of specific technical processes difficult or
seemingly undesirable due to fears that these discussions will rapidly
become obsolete as the technology changes. Two issues strike us. The first
is simple: there is always something to learn from prior practice, and no
shift in the technological apparatus of Digital Humanities research is
likely to be so great a rupture that all prior work is rendered irrelevant.
Secondly, Susan Hockey makes the point that “Humanities source material lasts for a very long
time and digital representations of that material need to be equally
long-lived”
[
Hockey 2012, 87]. She discusses the Text Encoding Initiative (TEI) which was set up
precisely to ensure this for the digital rendering of script sources. As
Hockey notes, “[o]ne of the original aims of the TEI was to create
an encoding scheme that would be independent of any particular
computing system”
[
Hockey 2012, 87]. Withdrawal of public funding, however, curtailed this effort and
expertise was thus lost. In this way, the speed of technological development
can lead to the silencing of discussions of its practices. Tensions exist,
for instance, between the development and commodification of technology on
the one hand, and its usability across time and space on the other: the
enhancement of a technology’s functionality typically correlates with its
accelerated commodification such that consumers clamour to buy the latest
machine and/or app, even if they are unable to fully use the ever-increasing
opportunities afforded by their current or new technologies [
Gerpott et al. 2013]. Academics may thus become increasingly
distanced from the technical experts that they require in order to undertake
DH projects as the gaps between known and unknown potential deepen, and at
an increasing rate.
3. The Nature of the Field
A third reason relates to the epistemic dimensions of the Digital Humanities:
do they constitute a discipline, or a set of methods or practices, or a
sub-set of enquiry? At present there is no ready answer to this question,
certainly no orthodoxy regarding DH’s domain. Texts such as Kirschenbaum’s
“What is Digital Humanities and What’s It Doing in
English Departments?”, the annual “day of DH”
(which aims to provide a snapshot of the field through the self-report of a
day in the life of various academics who identify themselves as Digital
Humanities researchers) [
Kirschenbaum 2010], and an entire
reader on the problems of definition [
Terras et al. 2014] all engage
with the issue of domain in different ways. The continuing need to
re-explore the boundaries may also contribute to a dearth of meta-discourses
around DH
beyond its definition, and that includes
meta-discourse on the processes of collaboration undertaken during DH
projects. Put simply, unless collaborations of all forms become a part of
what DH
is, then the field’s focus on definitional concerns
will contribute to the current lack of discussion.
4. Neoliberal Imperatives
A fourth reason for the under- and slow exploration of collaboration in DH
research concerns the neoliberal imperatives which govern contemporary
academe [
Strathern 2000] and which continuously flavour the
point above. Such imperatives have led to the simultaneous rise of
collective work
and individualized accountability in a context
of heightened competition – competition to be
“world-class”, “excellent”, indeed
primum without
pares. Where collaborative
research is submitted to the REF, for example, the collaborators have to
specify what exactly their contribution is. But such specification is
largely anathema to proper collaborative efforts and goes against our
understanding of creative thinking as a collective and intricately entangled
process involving a range of human and non-human interactions. It ultimately
denies the “co” of collaboration and, due to its
competitive underscoring, reinforces pre-existing inequalities between
different kinds of collaborators. As described above, this is partly
supported by the hierarchies within academe which construe certain staff as
subsidiary and which continue to instrumentalize both tools and people
rather than recognising them as co-producers of knowledge.
Resistance to changing such thinking is fed by a Humanities tradition that
locates agency, originality, and meaning-making firmly with the
author/maker, a tradition put to work in the justification of copyright
extensions which are publicly meant to protect the isolated,
“genius” artist and yet predominantly service large
corporations’ continued control over profitable cultural icons (see [
Lessig 2004]
[
Lessig 2008]). The central texts which rallied the various
turns that Humanities disciplines have undergone, including the celebrated
(if premature) declaration of the death of the author in favour of the
reader [
Barthes 1967], the gleeful reassertion of the author’s
cultural function in the renewed foci on intellectual property, paratexts,
and the myth of the persona [
Foucault 1969], and the various
interpretations of there being nothing outside of the text [
Derrida 1976], these texts each tend to reveal a struggle to
retain the pre-eminence of the human in the construction of meaning and
worth. These same texts also come to underpin a manner of thinking which
supports contemporary new materialist and posthumanist stances – the
author’s death reveals other factors that might play a role in
meaning-making; the lack of access to things outside of the text (or
context) does not mitigate their role in producing what we can perceive, and
how troubled and slippery text must always be demonstrates how we can be
fooled in our pursuit of truth; and the vagaries of economic and cultural
forces and the inscriptions and material properties of the authored and
edited book can show how non-human agency becomes entangled with human
activity. Neoliberalism’s focus on individual achievement leaves little time
for understanding subtle posthuman entanglements with people and things both
inside and outside of our full comprehension. In DH research, the non-human
actants of hardware and software are, in part, the secret ingredient – not
revealed by the master chef so that we might be delighted or surprised by
what we are served, but, rather, as in the story of “The
Sorcerer’s Apprentice”, an understanding that we may also find
ourselves unable to govern that which we call into being.
The Effects of Silence
The effects of not speaking about the collaborative processes that inform DH are
manifold. For one thing, such silences undermine a certain deliberative approach
to education and research which acknowledges the processes that inform that
education. This lack of acknowledgment means that processes are constructed as
inadvertent and hence beyond our agency. It fosters a “learned helplessness”
[
Seligman 1972] and leads to situations where academics in their 40s talk of themselves
as “dinosaurs” because they do not engage with, are alienated
from, and think they cannot understand contemporary technologies.
[21]
We see this effect most clearly at every academic gathering that involves
technology where, almost unfailingly, something (a PowerPoint presentation, a
video clip, the interface between laptop and data projector) does not work, the
presenter (usually a researcher or academic) does not know how to make it work,
and no technician to facilitate the process can be found. Such disjunctures and
competence issues are a norm rather than an exception in academe, and point to
divisions (human-human, human-machine, machine-machine) that the silence around
collaboration only serves to reinforce and exacerbate. It also has the effect of
individualizing this experience, suggesting individualized rather than systemic
failures to address and deal with such disjunctures.
At the same time, contemporary western culture is saturated with online tools
that both academics and students use, whether they do DH in a deliberative
fashion or not. In her interviews with staff in Sweden working in DH
environments, Griffin found that such staff often disavow DH as an academic
field precisely because of the ubiquity of digitality. Such ubiquity can lull
institutions into a sense of non-responsibility, where the mere existence of
Google-based online tools, for instance, can suggest that universities or
research centres need only participate in this cornucopia rather than consider
how this plenitude might require changes to their curricula, delivery, and
attitudes towards digitality. But purposive participation, such as we would
advocate, and which has to include discussions of collaboration at its core (for
instance of distance-collaborations with unknown others who produce tools, farm
data created through the use of those tools by scholars, and incite further
uses), is critical to becoming and being an effective researcher and scholar,
indeed a participant, in contemporary (digital) cultures. We would suggest that
the pervasive “fake news” and “fake media”
rhetoric of the current moment in part arises from the lack of a critical
digital humanities engagement we encounter in education and research scenarios
(see
http://blogs.lse.ac.uk/mediapolicyproject/2017/05/22/tackling-fake-news-towards-a-new-approach-to-digital-literacy/,
last accessed 7/10/2017).
Increasingly, digitality is recognized as a collaborative effort and effect,
where engagement always involves encounter with collaborators, known and
unknown. This means that digital literacy needs, inter alia, to analyse digital
collaboration that already occurs automatically, and its effects must be
addressed. We have hardly begun with this in the Digital Humanities. Failure to
address this increases digital divides between those seemingly in the know,
those who under-value the skills and knowledges they actually do have, and
others who become more and more technology-resistant as they feel increasingly
alienated. In contemporary knowledge economies, as well as in the technologizing
environments we inhabit, this is not a sustainable option.
A further institutional issue here is, as one of Griffin’s respondents put it,
that institutions have become used to the idea that Humanities ‘cost nothing’.
This respondent (the director of a DH laboratory that had eye-tracking
equipment, scanners, and a whole range of other electronic devices used to
conduct various kinds of DH research) had a strong sense that institutions
needed to be educated about the fact that the Humanities, and in particular DH,
also require capital resources to both establish and maintain themselves. And,
whilst there have been related critiques of the needs for certain kinds of DH
resources (e.g. [
van Zundert 2012]), the basic point that DH
requires resources is well taken and becomes more apparent in the context of
discussing collaboration since it refuses the rhetoric of the lone scholar,
needing only to be equipped with pen and paper, that continues to haunt the
Humanities. A related issue here is that institutions need plans for their
technology development which includes the re- and up-skilling of staff as well
as material resources. Such planning continues to be somewhat haphazard.
Rosenblum and Dwyer, for example, note that the fact that as co-directors of a
DH Centre they were given 25% and 50% time respectively – clearly not enough to
fulfil all their tasks.
Silence around collaboration contributes to maintaining a stagnant culture in
academe, in particular in relation to the fact that collaboration structures
learning. Machine-human collaboration occurs in learning all the time in that
knowledge producers at all levels constantly mobilize computer-based and
–produced knowledge to co-produce their own materials. At the same time the myth
of the singular individual who “discovers” continues to be
perpetuated in arenas such as prizes (Nobel as well as others), providing a
largely false image of how knowledge production occurs. This needs to change if
we want to normalize collaboration, the de facto reality of
knowledge production, for the coming generations of scholars.
Silence around collaboration also creates issues regarding our perception of the
object world, rendering it other and inaccessible. Whilst that world must always
be translated into human terms, and something is inevitably lost in such
translation, we miss too much in skipping it entirely.
[22] Our tools reinforce,
resist, shape, and encode material realities which both pre-exist, and are
co-produced by, them, and the better we are able to read these tools’
contributions to DH research, the better that we can understand that research’s
distortions and/or reasons for success.
If we think of DH as, at least in part, a collection of methods (which certainly
not every DH scholar does), then explaining how collaborations might come about
and be successful (or not) can be important ways of enabling others to learn
from the processes of DH projects. The development of a large body of
methodological literature around interviewing, for example, has produced
important insights into the dynamics between interviewers and interviewees,
leading to a foundational example of the contemporary notion of knowledge as
co-produced and situated [
Hartsock 1998]
[
Griffin 2016]. This understanding, in many ways, goes against DH
traits such as the standardization of knowledge into readable data, as an effect
or requirement of digitization, and the deterritorialization of knowledge that
this implies, leaving DH, at times, troubled by the tension between variability
and standardization [
Flanders 2012], between the unpredictable and
the prepared for, the unintended and the deliberate. Unstructured data are near
impossible to digest digitally, but this does not mean that digital research can
be blind to their effects.
Not discussing human-human collaborations is also a way of implying a
“them” and “us” structure of academe
where different categories of contributors occupy different and hierarchized
spaces. This results in a reinforcement of the failure to transform the social
and political structures of the academy, something that is necessary both in
order to better realise the collaborative and interdisciplinary work to which
universities aspire and to the realities of such work.
At the same time, most scholars inadvertently collaborate with technology
already, at least minimally through Google searches, online reading, emailing,
Skypeconferences etc. Mystifying the collaboration process in DH means
constructing resistance to technology by making it into the unknown. This is
also reinforced by a lack of recognition in higher education institutions that
digital competence is a skill that needs to be acquired and maintained in a
manner akin to Sennett’s craftsman’s training. Such training is often
unavailable in academe, not recognized in terms of time and work requirements,
and left to the perspicacity and perseverance of individual staff. This results
in a DH skills shortage that currently manifests itself in the limited numbers
of staff knowing limited numbers of DH tools. In turn, this means that the
development of new research ideas and related critiques of DH cannot flourish
nor can the collaborations that makes the best DH research so stimulating. It’s
time we talked more about these things.
Moving Forward
How, then, could the discussion around collaboration in DH change? As we pursue
our own collaborations, that will eventually be written up and published, we’re
starting to ask how we can best be true to the issues raised in this article.
Matt is involved in two projects that have technical, creative, and other
non-academic partners and which might function as examples.
The first,
Ambient Literature[23] sees professional writers commissioned to
create works of electronic fiction that investigate our relationship with both
places and digital technologies.
[24] The project will also result in a co-authored academic book,
with no lead author, that includes contributions from, and interviews with the
writers and technicians working on the pieces. There will, further, be a
“cookbook” aimed at demonstrating, for those interested
in the production of these kinds of works, the options and opportunities
currently available, i.e. what tools, technicians, budgets, and readers will
permit. An edited collection will further gather voices from across writing,
publishing, technical production, and academia. Maybe the project is cheating –
it was devised to investigate the ways in which collaborations between humans,
technologies, and environments manifest and alter in a landscape of ubiquitous
mobile computing. But it demonstrates what voices should be included and the
balance of power that might be achieved; no one academic gets to be the
primus deploying everyone else as instruments of knowledge
production.
The second project is an augmented reality performance of Sir Gawain and the Green Knight, a medieval text set in the West
Midlands. The project is at the earliest stages of development, but as heritage
and technical partners are sought (the first to provide an appropriate location
for the production, the second to enable the movement of characters, sounds, and
settings between real and virtual environments), it is clear that, again, the
roles of academic, non-academic, technical, and non-human collaborators will
need to be carefully considered. It would now seem impossible to think of the
work simply accruing to the two academics on the project team, with all other
actants simply being instruments for them to deploy – the team will
have their say, and when it comes to the silenced roles of the non-humans, be
they mobile phones, buildings, or environmental features, there will be at least
some attempt to speak up on their behalf.
Maybe creative productions set the easier precedent; the industrial arts of
cinema and videogames, despite the impositions of the auteur, are well
recognized as mass collaborative efforts, with cameras, lenses, and sound,
graphics, and physics engines all frequently brought to the fore – you could not
have seen or played this without all of these people, and all of these
things. But in any project, we think that it is relatively
easy, and undoubtedly important, to resist the silencing outlined above. It
boils down to two calls: the first is to recognize that no collaborator can ever
be neutral, and the second that, therefore, their roles must be understood as
well as possible, before, during, and after the event. Credit and blame need to
be attributed, expressed, and shared. That’s how we get better.
Acknowledgments
The authors would like to thank the anonymous reviewers for their extremely
helpful comments.
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