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Social Studies of Science
http://sss.sagepub.com/content/33/4/539
The online version of this article can be found at:
DOI: 10.1177/0306312703334003
2003 33: 539Social Studies of Science
Paul Jeffrey
Research Collaboration
Smoothing the Waters : Observations on the Process of Cross-Disciplinary
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ABSTRACT Although both research funders and knowledge users continue to call for
more and higher-quality collaboration between researchers from different disciplines,
there is little evidence available to inform the structure and management of cross-
disciplinary research teams. A descriptive account of cross-disciplinary collaboration is
presented based on a study of a cross-disciplinary team researching natural resource
degradation issues. A number of tools are identified that characterize and support
the collaboration process, including the use of story-lines and metaphor, choice of
vocabulary, the nature of dialogue and the role of mediating agents. Four products
of collaboration are also identified: ‘process’, ‘understanding’, ‘utility’ and
‘knowledge integration’. Conclusions focus on the implications for research
programme design and the content of research training curricula.
Keywords collaboration, cross-disciplinary research, research management, research
teams
Smoothing the Waters:
Observations on the Process of Cross-
Disciplinary Research Collaboration
Paul Jeffrey
It has been said variously that Aristotle, Leibniz, John Stuart Mill and
Thorstein Veblen was the last human being to have known everything.
Irrespective of the accuracy or indeed the significance of these claims, the
anecdote does emphasize the increasing breadth and depth of scientific
knowledge that has resulted in the emergence of a myriad of disciplines or
branches of learning, each one requiring many years’ study to reach the
forefront of research. This ever increasing specialization has prompted
many to consider how disparate scientific contributions can be rebuilt or
integrated to provide solutions to (or at least help us understand) the
complex challenges which face our communities. Such cross-disciplinary
research is now almost ‘de rigueur’, even though we remain largely
ignorant of the determinants of good practice, or the effective translation of
its output into policy and decision support tools.
A central motivation for research funders to support studies that
consider the contributions of more than one disciplinary field is the fact
that real-world problems do not come in disciplinary-shaped boxes. In-
deed, national research policies lay increasing emphasis on problem-
oriented research, which requires the crossing of disciplinary boundaries
Social Studies of Science 33/4(August 2003) 539–562
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(Weingart & Stehr, 2000). Clearly, the complexity and sheer magnitude of
information and knowledge emerging from interdisciplinary research activ-
ities need structuring, and the linkages between the various contributions
identifying, if the full utility of such studies is to be enjoyed. Consideration
of this problem in theoretical terms (Reiser, 1958) has largely given way to
investigations of the practical problems involved. Other writers have high-
lighted the significant role played by communication in cross-disciplinary
teams (Berkenkotter & Ravotas, 1997; Wear, 1999), the motivations of
researchers who engage in collaboration (Melin, 2000), and the influence
of group dynamics on individual and team performance (McCorcle, 1982;
Johnson & Johnson, 2000).
Although interdisciplinary research has become fashionable amongst
both funders and practitioners, its methodological development has been
constrained by the knowledge base being itself dispersed amongst a variety
of disciplines. There remain significant difficulties in turning cooperation
(working together for individual ends) into collaboration (working together
for a common end). Ambiguous use of terms by single-discipline scholars
and a dearth of progressive writing on interdisciplinarity as a subject have
resulted in little substantive progress being made in providing integrated,
cross-disciplinary assessments of the challenges which face our commun-
ities.1In particular, science as a problem-solving method has failed to
make a distinction between contributions that are simply edited summaries
of a sequence of isolated research activities, and contributions which
provide genuine integration through collaborative working and common
methodological frameworks.
The term we adopt here for all forms of collaboration between
researchers with different educational backgrounds is ‘cross-disciplinary’.
Rossini & Porter (1984) propose a three-way classification of cross-
disciplinary studies. They see ‘Multidisciplinary Research’ as comprising a
number of independently performed studies with external coordination
through appropriate editorial linkages. ‘Transdisciplinary Research’ is con-
sidered to include the development of an overarching paradigm that
encompasses a number of disciplines and (latterly) stakeholder groups.
Finally, ‘Interdisciplinary Research’ falls between the two previous ap-
proaches: components being linked internally and substantively without
being subsumed under a supradisciplinary paradigm.
Recent writings by the group of theorists centred on Michael Gibbons
(for example, see Gibbons et al. [1994] and Nowotny et al. [2001]) have
provided the transdisciplinary paradigm with a socially significant func-
tion. They suggest a radical reform of the organization of science, moving
from work that is defined in relation to the cognitive and social norms that
govern academic science, to knowledge produced in the context of applica-
tion. So, instead of producing knowledge within the discipline, research
teams become transdisciplinary and heterogeneous. Reactions to this ana-
lysis have claimed that the changes described are neither distinctive nor
historically unique. Furthermore, critiques of the implications for science
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have shown how our understanding of interdisciplinarity is very closely tied
to epistemological assumptions about the relationship between science and
nature, and the structure of science and scientific disciplines (Weingart,
1997). The debate has fuelled calls for a greater degree of collaboration
across the science–society boundary as well as across disciplinary bound-
aries (Klein et al., 2001: 7).
Locating the material reported later within a broader intellectual
tradition is problematic.2The literature on cross-disciplinary interaction is
fractured by competing explanations focused on theory, praxis, language,
understanding and action. This contribution does not aid clarification or
simplification of these contending descriptions (although we will briefly
discuss their salient points as they pertain to observed processes). How-
ever, through monitoring and analysing the process and experience of
collaboration we feel that we have some valuable comments to offer
concerning the relationships between inquiry frameworks and research
method. There are, as yet, few case studies available that can provide
evidence-based, prescriptive guidance for research managers at both pro-
ject and programme levels. Such studies are a simple yet essential step
towards greater understanding of cross-disciplinar y research processes.
Method
The evidential material upon which the findings and conclusions of the
present paper are based was collected during an 8-month period, during
which the investigator was asked to act as intermediary for, and report on,
the process of interaction within a cross-disciplinar y research team. The
research team were working on a project funded by the European Com-
mission that focused on exploring the determinants of, and responses to,
desertification processes in Southern Europe. In particular, a sub-group of
the research team was asked to prepare a micro-simulation model of crop-
choice dynamics in a case study area of Southern Greece. This group
initially consisted of 10 researchers from a variety of disciplinary back-
grounds: sociology, agronomy, anthropology, archaeology, biology, simula-
tion modelling and computer science. However, during the initial phases of
the project, the team became polarized into two factions: social scientists
and simulation modellers. The intermediary had a background in ‘Science
and Society’ and ‘Socio-Natural Systems’ and was of a relatively junior
academic status, but had worked previously with several of the research
team (both modellers and non-modellers). Primary data on interactions
between the researchers were collected through observation and annotated
records of the collaborative working meetings (42 h) and two interviews
with each of the 10 members of the team (one mid-way through the project
and the other on completion). This model of primary data acquisition
clearly has restrictions, although it has been used effectively both in
isolation and as part of multi-method approaches in previous studies of
this type (for example, see Anderson [1992] and Newell & Swan [2000]).
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The micro-simulation model developed by the project described a
multi-function assessment of various economic, technological and environ-
mental constraints affecting farmers’ choice of crop. Decisions regarding
changes in agricultural land usage have an immediate and widespread
influence not only on the physical and chemical properties of the soil, but
also on its relative productivity. The simulation algorithm used in the
model was based on the use of a probability density function in 13
dimensions that governs crop-choice behaviour on a theoretical representa-
tion of the farming landscape. Social enquiry data (from surveys and
studies carried out by the social scientists) were to be used to characterize
the behaviour of the transition matrix: for example, what motivated
farmers to change crop, modify agricultural practices, or indeed, get out of
farming altogether.
Both sets of researchers agreed that a representation of the farmers’
cognitive process when deciding on crop change constituted the focus of
effort. In other terms, the cognitive background and contextual determin-
ants of crop-change decision-making were to be preferred over an approach
that emphasized the process of coming to a judgment on crop selection.
Hence the activity could be defined as one whereby the knowledge of
farmers’ attitudes and behaviour, as monitored by researchers in the field, is
elicited from the social scientists and formally structured in a format that
enables it to be included in a computer-based simulation model.
As noted earlier, relationships between the disciplines were informally
monitored during the project, with the aim of developing a critique of the
interaction between the various disciplinary representatives. In particular,
attention was focused on areas where common understanding was re-
stricted and where genuine integration of knowledge proved difficult to
achieve. The intellectual dynamics between the various groups of re-
searchers (as reported later) has implications both for disciplines in isola-
tion and for interdisciplinary research as a whole. Whilst it might be ill-
advised to term the relationship between the various traditions involved in
the project as ‘synergetic’, a circumspect observer might point out the
essentially dialectical nature of the discourse and label it ‘mutually
informative’.
Structuring a commentary on the nature and significance of collabora-
tion between different scientific disciplines calls for a frame of reference.
Without a framework within which to comment on the observed inter-
action, this text would be in the format of reportage, simply describing the
sequence of events without identifying their relevance. The task is therefore
to select an appropriate frame of reference or thematic structure that can
be used to interpret the observations. Several candidates for such a
structure are available from other academic fields. For example, the study
of organizations could be considered as an appropriate starting point, as
cross-disciplinary collaboration involves similar types of process. The same
could be said of group dynamics and some branches of education theory.
However, the types of individuals involved, and the nature of the activities
taking place, are significantly different in the case of cross-disciplinar y
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collaboration. In addition, we are seeking a framework that will allow us to
look beyond issues of control, personal interaction and decision-making, to
identify areas of collaborative success/failure as perceived by both an
observer and the members of the research team themselves. Because we
would like to involve the research team in a debate about collaboration, the
way in which we describe its constituent elements also needs to be readily
comprehended and understood. Consequently, there is a need for a
measure of simplicity and transparency for any thematic structure we
might adopt.
The precise thematic structure selected for use in the present paper is
therefore intentionally straightforward and uncomplicated, highlighting the
means by which collaboration is accomplished (tools) and the outcomes of
collaboration (products). Conversations with members of the research
team about collaboration across the disciplines were typically focused on
the means and output of collaboration, indicating that these are topics of
concern and significance to the collaborators themselves. Hence, using a
thematic framework that highlights the tools and products of collaboration
has relevance to the project team and is also of sufficient scope to allow a
broader discussion of the more abstract features of collaborative
activities.
In detail, four products are identified, each one representing a specific
feature of the collaborative process. These products are denoted as ‘pro-
cess’, ‘understanding’, ‘utility’ and ‘knowledge integration’. Tools of collab-
oration are devices and mechanisms that are utilized to achieve these
products. Relevant tools include the use of ‘story-lines’ and ‘metaphor’, the
choice of ‘vocabulary’, the nature of ‘dialogue’ (in this case, negotiation)
and the role of ‘mediating agents’. This is clearly not an exhaustive listing
of relevant tools and products. However, the specifics of the disciplinar y
interactions observed over the 8 months of the study suggest that the tools
and products itemized provide a sufficiently rich framework within which
to discuss the issue of interdisciplinary collaboration. The relationship
between tools and products is hierarchical: the tools of collaboration can
be seen as conduits for the achievement of products. Furthermore, it is
important to note that by discussing cross-disciplinary collaboration in
terms of tools and products, we do not seek to propose a formal theoretical
structure for analysing the process. Indeed, if there is a theoretical con-
tribution contained in this paper, it is in the disaggregation of collaboration
into component elements (products).3
The Collaborative Process
As noted earlier, the task objective for the collaborating team was to
produce a simulation-based crop-change model based on primary data of
farmer decision-making considerations. In particular, issues of time scale
(how often farmers make a decision about, for example, irrigation), the
options for change and the influences these changes may have, were
considered of immediate significance. Although an initial attempt was
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made to use diagramming to support interaction between the two groups,
the discussion soon became disjointed and unfocused. The primary reason
for this was that the variety of behaviours and decision-making criteria that
the social scientists had recorded in the field confounded attempts at a
structured, simplified formulation. It is worth noting that this temporary
breakdown in effective communication between the modellers and social
scientists (despite the efforts of both sides to maintain a constructive
dialogue) was unrepresentative of the relationship as a whole. It did,
however, have a secondary effect as part of a process that saw a reduction
in the number of disciplinary representatives actively engaged in this
element of the project. Although logistical (location) and resource (fund-
ing) constraints were also contributing factors, it was evident that this early
impasse in the production of a common vision for the work led several
researchers to withdraw from the process.
Perhaps sensing that the framework used for eliciting data for the
model needed to reflect some kind of formal decision-making structure,
both sides moved towards an approach that involved breaking the decision
down into a number of components. A series of sub-system diagrams were
sketched, each of which related to a distinct decision issue: for example,
the decision to irrigate, the decision to pump water, the decision to operate
a windmill (as protection against frost), the decision to add fertilizer. By
using these cause–decision–effect tables, a series of decision trees could be
formulated, each one relating to a specific decision issue. The link between
the decision trees and the modelling activity is clear. The structure and
format of the decision tree is readily coded as a sequence of ‘IF’ and ‘AND’
rules.
Structuring a set of decision trees in this way was not considered to be
technically demanding. However, two aspects of the approach caused
disquiet amongst the social scientists. First, any single representation
constituted a unique mode of behaviour. Although the modellers explained
their intention to instil diversity of behaviour by applying ‘noise’ to the
process, the social scientists were concerned that the qualitative nature of
variety would not be captured. In particular, there was a feeling that a
data-driven representation of the mechanisms that generate variety of
behaviour was required rather than a numerical simulation of the variance.
Second, although the approach provided opportunities for the crop change
element of the model to be integrated with the physical (soil, climate) and
economic elements, the range of variables being considered was unreal-
istic. In the words of one of the social scientists, ‘Farmers just do not go
through that thought process every time they want to irrigate their land’. In
an attempt to structure the decision issue in a more formalized manner,
the level of abstraction had become too far removed from what the social
scientists regarded as being acceptable.
During a discussion of the details of the computer model itself, it was
suggested and agreed that a possible alternative structuring technique was
to use a transition matrix. Completion of the cells within the matrix
involved the social scientists considering each cell of the matrix in turn,
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and determining an appropriate value between 0 and 1 (representing the
possibility of crop change). However, the sheer size of the task and the
often detailed considerations that were involved in coming to a determina-
tion of the probability of crop change made the activity difficult to manage
and impractical. Whilst some attempt was made to make progress, it soon
became evident that the social scientists found it difficult to maintain a
consistent focus on the determinants of crop change and that the set of
four bi-modal descriptors being used (Farmer type – full time/part time;
Soil quality – good/bad; Location – central/periphery; Irrigation available –
yes/no) did not even begin to capture the context of , or influence on, each
possible crop-change event.
On reflection, the central dynamic of these attempts to formalize the
crop-change decision was focused on pinning down the appropriate level of
variety for inclusion in the model. A caveat to this goal was the concern
that elicitation of the information (in whatever format) had to be a
manageable, understandable and achievable process. In another, more
uncompromising sense, the debate concerned the representation of variety
and, perhaps more significantly, responsibility for handling variety. The
computer modellers often spoke of introducing ‘noise’ into the system
without clearly explaining the mechanism by which they intended to
achieve it. They also continued to emphasize the point that variety could be
built into the model at a later stage, following resolution of the model’s
structure and identification of key variables/relationships. Similarly, the
social scientists spoke of the difficulties and dangers associated with
standardizing the representation of behaviour. Simply by the act of verbal-
izing a specific mode of behaviour, they committed themselves to a
representation that they knew was possibly unique and ungeneralizable.
Two distinct, and contrasting, methodological systems are evident
here. The very nature of computer simulation imposes a structure on both
the type of analysis and information that are to be used. Analysis routines
must be sequential and transparent (every event must have an identifiable
precursor). Data must be ordered and classified. In contrast, the knowl-
edge possessed by the social scientists (concerning farmers’ perceptions of
crop change) suggests at best a semi-structured process. The decision
process, if there is one, is opaque and the contributing considerations are
often confused and variable across individuals. A valuable observation in
this context (made by one of the social scientists) was: ‘Will the nature of
the variety that the modelling activity achieves match that observed by the
social scientists in the field?’
The desire of both sets of researchers to make progress on the project,
and the appreciation that what was being attempted constituted a non-
trivial activity, encouraged a pragmatic approach to the work. However,
there remained an underlying sense of scepticism (bordering at times on
suspicion) from the social scientists in particular, regarding the representa-
tional aspects of the proposed computer model. For example, just how
much variety could be built into the model as ‘noise’ and how much could
be handled at the level of formal rules and transition functions was an
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ongoing topic of concern: the social scientists feeling their way towards a
representation that exhibited some structural correspondence with the
farmers’ (assumed) cognitive constructs. In addition, there was a feeling
amongst the social scientists that the variables that had been selected for
inclusion in the activities to date had been arrived at arbitrarily. For
example, the four bi-modal descriptors highlighted earlier were chosen
without any obvious defence for their preference over others.
At this point, a consensus began to emerge within the group of social
scientists for a hybrid approach to the structuring of the crop-change
model based on the concept of multiple filters. These would be in the form
of two sets of templates overlying a geographical map of the region. The
first set of templates would indicate the physical, chemical and hydro-
logical limitations to crop choice (that is, what you can and cannot grow on
a plot). The second set would indicate farmer-specific considerations such
as cost, technology access and labour availability. The various templates
would then be used to generate a dynamic model of crop choice with
interactions between the physical, economic and other variables. In effect,
such an approach passes on responsibility for the handling of variety to the
computer modellers. It allows the social scientists to concentrate on a
broadly applicable structure for the crop-change decision issue and the
identification of the relationships between the contributing factors as
perceived by the farmer. The preferred technique with which to capture the
farmers’ perception of the decision issue was to apply an adapted version
of the decision-tree approach. The decision-tree structure was to be driven
by interview data and representations of variability were to be based on
farmers’ stated biases. The aim here was to derive a representation of the
farmer’s consideration of the issue by identifying both its constituent
elements and the relationships between them. The term ‘constituent ele-
ments’ infers those aspects of the decision issue that are considered by the
farmer. For example, in the case of the crop-change decision, relevant
factors might include the suitability of the soil, the existence of a potential
market for the crop and expected income levels.
This broadly supported approach to capturing illustrative crop-change
decisions represents a compromise between the uncoordinated demands of
the two sets of researchers (modellers and social scientists). Perhaps it
would be more accurate to say that it allows two incongruous approaches
to be ‘reconciled’ rather than ‘integrated’. The problem, as throughout the
process, was one of capturing variety. It was impractical to collect data that
would enable each farmer on the landscape to be represented as an
individual, or to code all the farmers as individual agents within a simula-
tion tool. However, it was also undesirable to simply apply a stochastic
function to represent the distribution of behaviour amongst farmers.
Hence, some middle ground needed to be found which allowed diversity of
perception and subsequent behaviour to be represented in a format that
was both authentic and pragmatic. By using the notion of a ‘prototypical
farmer’ as the basis for representing a hierarchical and relational structure
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for the decision issue, the collaborators were able to achieve a standard
format but maintain the potential for the representation of variety.
Results from the Study
Before relating the details of the study’s findings in terms of the tools and
products of collaboration, it is worth outlining two issues that dominated
discussions within the cross-disciplinary group. The first of these issues
concerns the divergent preferences for a representational structure in the
model. From the computer modellers’ viewpoint, the decision as to which
mechanism to adopt for representing the farmers’ decision framework was
purely a function of whether the relevant processes could be structured and
quantified (although this raises its own problems as will be discussed later).
Conversely, the social scientists’ desire to present a ‘true’ picture of the
farmers’ cognitive processes made them implicitly favour a decision-tree
approach over a transition-matrix approach. Second, the intended function
of the computer model and the consequences of this for the type of
information required from the social scientists emerged as central areas of
misunderstanding. Those working on the modelling side of the project
were keen to emphasize the distinction between bounding the actual and
bounding the possible. In context, the distinction here is between develop-
ing a model that faithfully represents the farmers as they behave with
regard to crop choice now, and developing a tool with which we can
explore the opportunities and options that might be open to the farmers,
and implying from those trajectories a set of policy implications. This
distinction is an integral part of iterative approaches to modelling, where
model design is initially of an elementary and uncomplicated nature,
complexity and detail emerging from iterative development of the model’s
structure and dynamics. In addition, given the role of the model as an
exploratory tool for investigating the dynamics of change in the case study
region, some indication of what is possible clearly determines the bounds
of behaviour. From the practical viewpoint of achieving some progress in
melding the knowledge of the social scientists with the intentions of the
computer modellers, the distinction between actual and possible behaviour
provided an ongoing source of diversion.
We now turn to a discussion of those tools (vocabulary, metaphor,
story-lines, negotiation, role of intermediary) that were identified as being
fundamental to the achievement of the products of collaboration (process,
understanding, utility and knowledge integration). A brief summary of the
products themselves will then be provided.
The Role of Vocabulary
Vocabulary refers to the words that we use to communicate the meaning of
our thoughts. The variability of experience and learning, and the magni-
tude of available expressions generate a myriad of possible vocabularies.
Science and scientists, driven by the need to distinguish, define and
describe, are infamous for developing new vocabularies. It is not surprising,
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therefore, that when representatives from two different scientific disciplines
meet, they have difficulty making themselves understood. Ideas generated
within one discipline may make perfect sense within the discourse that they
came from. However, they will be evaluated in the context of acts and
practices that do not function according to the discursive logic of the
original discourse. Hence, even though the representatives of different
disciplines may be discussing a single, unambiguous topic, their vocabular-
ies may be very different and mutually perplexing.
It can be appreciated that collaboration between the disciplines is
hampered by the absence of a collective and comprehensible set of refer-
ence terms. Indeed, one of the most significant processes observed during
the development of the micro-simulation model was the evolution of a
common vocabulary. Propelled by the use of simile, analogy and meta-
phor, this shared language set was, in fact, an integrated version of the
individual vocabularies used by each group. Both the social scientists and
the modellers adopted words, terms and phrases from each other’s vocabu-
laries with varying levels of success. Interestingly, the meanings of certain
terms and phrases were subtly altered as they moved across from a
discipline-specific to a common vocabulary, creating a small collection of
terms that had an interpretation unique to the cross-disciplinary activity
itself.
The evolution of a common vocabulary was observed as being influ-
enced by several factors:
• Size of group – as the collaboration progressed and the size of the
contact group diminished, so the rate at which a common vocabulary
developed, increased.
• Presence of an intermediary – the intermediary who worked as part of
the contact group had previously worked on both social science and
modelling-related projects and was able to facilitate the development
of a common vocabulary.
• Listening – a willingness to ‘shut up and listen’4clearly allows other
actors to articulate a point and introduce possibly useful words into
the common vocabulary.
• Dedicated and focused working sessions – these limited the amount of
‘noise’ that tended to accompany cross-disciplinary interaction.
• Discussion about the use of the model as well as the design of the
model – providing opportunities for individuals to extrapolate their
understanding of the model from setting the structure to manipulating
its functionality, allowed the emerging common vocabulary to be
placed in a relevant context.
• Use of diagrammatic representation – the use of drawings, graphs,
sketches and other pictorial representations helped to clarify disputed
or unclear descriptions.
• Respect for the limitations of one’s own and others’ understandings –
despite its critical role, this is a somewhat difficult factor to capture in
terms of specific actions. A willingness to explain things from first
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principles if need be, and an openness about one’s own ignorance are,
however, cornerstones of an ongoing and productive cross-disciplinary
relationship.
Perhaps surprisingly, the development of a common language set was
not observed to be a significant hurdle to cross-disciplinary collaboration.
Individuals from both the modelling and social-enquiry groups prompted
their colleagues to clarify words and phrases with which they were un-
familiar. Although this process of developing a mutually comprehensible
vocabulary slowed down the pace and often interrupted the flow of
discussion, it became a decreasing burden on dialogue and eventually
ceased to be a significant hindrance. It should also be noted that the
emergence of a mutual vocabulary and its use as a catalyst for collaboration
can be cited as a positive contribution to the project’s aims.
One final general observation should be made here concerning this
element of the interaction. The task of explaining and clarifying misunder-
stood terms was carried out within an informal communicative structure
that progressed from verbal definition, through verbal example to non-
verbal representation. Most queries were dealt with simply by providing a
spoken, formalized definition of the term or phrase. In the event that this
was not sufficient, recourse was made to examples, comparisons and
analogies. Finally, if misunderstanding persisted, diagrammatic or pictorial
techniques were used, and the help of other contact group members was
solicited.
The Role of Metaphor
The significance of metaphor in linguistics is already well established, and
there is a long-standing and detailed literature concerning its use as an aid
to understanding and thought.5However, within the context of collabora-
tion between scientific disciplines there are a number of issues which were
found to be of particular interest.
Information flow between the two disciplines involved in the micro-
simulation model development (and the subsequent need for under-
standing) occurred in two distinct phases. Through dialogue, there was a
continual flow of information in both directions. Initially, however, there
was a bias in the need for understanding as the modellers struggled to
formulate a structure for the model that matched the nature and avail-
ability of the data. Later in the study, the bias was reversed as it became
important for the social scientists to understand what the model was and
was not capable of representing. Metaphors were exploited extensively by
both groups during these processes. Interestingly, the use of metaphor was
discernible as a tool for both explanation and query, and ‘dominant
metaphors’ were referred back to when actors felt that they were losing the
clarity of an explanation.
The term ‘dominant metaphor’ indicates a metaphor used on numer-
ous occasions to illustrate different points. Such metaphors were in-
crementally developed over the course of several encounters, gradually
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increasing in complexity and yet becoming less abstract and closer to a full
description of the source phenomena. Clearly, as some metaphors became
dominant, so the integrity of understanding between the disciplines be-
came dependent on a few, core analogies. This trend did not appear to be
detrimental to continued and growing understanding, although it did have
the effect of bounding the range of communication tools available.
Metaphor usage amongst the contact group was also highly explicit.
References to analogous or similar phenomena to those being discussed
were prefixed or suffixed with a clear indication that metaphorical language
was being used. For example the expression ‘you can imagine the template
as a sort of filter through which only certain things pass’ would be typical
of this style of metaphor use. The terms ‘sort of’ and ‘imagine’ make it clear
that a substitute for the real thing is being referred to. Having encountered
similar approaches to explanation during their education, such measured
use of metaphor may well be a characteristic of academics in general.
Finally, it was observed that all parties succumbed at various times to
using easily understood but otherwise inappropriate metaphors. These
were often simple analogies, possibly even single words, the possible
misunderstanding of which had not been considered.
The Role of Story-Lines
The role of story-lines in promoting understanding has only been raised
relatively recently in the literature. They have been described as a ‘gen-
erative sort of narrative that allows actors to draw upon various discursive
categories to give meaning to specific physical or social phenomena’
(Hajer, 1996: 56). With reference to the issue of collaboration between the
disciplines, the observed interaction during the study provided evidential
support for the following features of story-lines.
• They have the functional role of facilitating the reduction of the
discursive complexity of a problem and creating possibilities for prob-
lem closure.
• As story-lines are accepted (more and more actors start to use them),
they get a ritual character and give a certain permanence to the
debate.
• They allow different actors to expand their own understanding and
discursive competence of the phenomena beyond their own
discourse.
• They provide a narrative that allows actors from different disciplines to
illustrate where his or her work fits into the jigsaw.
Hence, story-lines provide actors with a set of symbolic references
(expressed through language) that suggest a common understanding.
Indeed, some authors have interpreted the phenomenon of story-lines as
having a central contribution to make towards collaboration in its broadest
sense. They see them as fulfilling ‘an essential role in the clustering of
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knowledge, the positioning of actors, and ultimately in the creation of
coalitions amongst the actors of a given domain’ (Hajer, 1996: 63).
Although most of the theoretical work on the use of story-lines has
been carried out in relation to the fields of policy studies and politics, there
are significant areas of application to the understanding and promotion of
cross-disciplinary collaboration. For example, whilst there is little rele-
vance in the policy-focused process whereby story-lines promote a dis-
cursive coalition, the disciplinary actors involved in the micro-simulation
study all needed to both understand and be able to articulate (express
either verbally or with diagrams/text during discourse) the same data
structures. Hence, there is a commonality of welfare in both cases.
Furthermore, the use of certain vocabularies (and more significantly of
metaphor per se) in cross-disciplinary discourse bestows a pseudo policy-
related language set on the interaction. Actors use abstract and conceptual
images akin to those typical of policy-level debates.
The Role of an Inter mediary
We would make two points regarding prerequisites for the successful
functioning of an intermediary. First, it is dependent on an acceptance by
all parties of the mediator’s integrity and good will. The disciplinary groups
need to believe that the intermediary is a credible and competent indi-
vidual, and that he or she has the best interests of the project as a whole
at heart. Second, an intermediary needs to be able to communicate
effectively with all concerned parties. Experience of operating intellectually
in more than one disciplinary area is therefore desirable, as is some
knowledge of cross-disciplinary knowledge integration issues.
During the project, an intermediary was able to facilitate the process of
collaboration by:
• Maintaining a focus on the collaborative aspects of the study. Cross-
disciplinary collaboration was permanently at the top of the inter-
mediary’s agenda.
• Being able to force an issue without damaging the relationship
between the disciplines. If progress was being held up, the inter-
mediary could take a difficult or potentially unpopular decision. Any
resultant antagonism or enmity would be focused on the intermediary
and not on other members of the contact group.
• Assessing the relevance and value of suggested activities. Through
having experience of both disciplines, the intermediary was able to
identify potentially counter-productive methodological or technical
initiatives.
• Assisting in the development of a common vocabulary and interpreting
the use of metaphor (see earlier).
In addition to these generally constructive components, there were a
number of negative aspects that were also identified. The most significant
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of these was that the intermediary is prone to being drawn into the
collaborative exercise as an actor (for example, performing a data inter-
pretation exercise rather than facilitating data interpretation).
The Role of Negotiation
It would be deceptive to claim that negotiation was an intended element of
collaboration in this study. Indeed, we can only denote the nature of the
observed dialogue as ‘negotiation’ in retrospect (apart from in one specific
case which is dealt with at the end of this section). Having carried out a
limited ‘debriefing’ of the researchers involved in the study, we find that
there has been a strong perception on their behalf that the model’s
structure and content have been negotiated between the social scientists,
modellers and intermediary. We would point out that whilst negotiation
may not be the only or the best form of dialogue between the disciplines, it
was the dominant form here as perceived by the participants.
Negotiation is a search for agreement, often through a process of ‘give
and take’, where each side relinquishes some ground on one issue in
exchange for gaining ground on other issues. That the nature of model
development should be described in this way may initially appear both
unusual and inappropriate. However, given the intellectual backgrounds of
the two disciplines concerned, the emergence of an essentially dialectic
process is not surprising. Although the relative effectiveness of negotiation
as a form of interaction in this case is difficult to assess, there are several
dimensions of the process that we would highlight at this stage.
First, negotiation promoted the development of a plausible model.
Frank and open discussion of each participant’s own impression of the
model’s form and content resulted in consensus regarding what was
feasible and achievable within the timescales available. Achieving such
consensus often involved team members bounding the intentions or ex-
pectations of others (for example, by drawing attention to the limitations of
data sets or modelling techniques). Second, negotiation promoted a par-
ticular form of verbal interaction that tended towards debate as opposed to
discussion. Interaction was driven by relevant issues or problems. Partici-
pants had distinct positions on these issues which they stated early on,
inviting others to do likewise, leading to the essentially dialectic process
alluded to earlier. Finally, the negotiated form of interaction created its
own dynamic by maintaining contact between participants where it might
otherwise have collapsed. Because the final act of negotiation is agreement,
there was little opportunity for one party to withdraw completely from the
process. Dialogue continued even when consensus was elusive.
As noted earlier, there was one aspect of the cross-disciplinary inter-
action that could clearly be identified as ‘negotiation’ during the study
itself; the desire by both sides (sociologists and modellers) to negotiate
responsibility for complexity. The data set collected by the social scientists
contained a large amount of information, both in terms of the number of
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attributes and the types of links between attributes. A relevant and complex
story-line (see earlier) had already been formulated describing the percep-
tions recorded during the survey work. Capturing and representing the
complexity of farmer perceptions and behaviour was an agreed objective.
How this was to be achieved was less clear and became an issue that was
sporadically and indirectly negotiated during the early stages of model
development.6
Negotiation as a dominant discursive style between the disciplines was
also evident in the later stages of the project, particularly during project
reporting. As noted later, several different collaborative composition styles
were evident, each one providing opportunity for negotiating the content
and structure of the final product. Where interaction was at the level of the
statement, negotiation tended to focus on the factual consistency and
meaning of the text. Contrastingly, where interaction was at the thematic
level, negotiation was concerned with the synergy between the various
contributions. If comparative utility is to be allocated to these two collabor-
ative writing styles, then practical collaboration at the thematic level
appears to hold more potential for exploitation.
Having discussed the tools of collaboration, we now move on to
address the products of cross-disciplinary collaboration. These outcomes
are proposed as a non-exhaustive catalogue of benefits emergent from the
particular research process discussed earlier. They are necessarily abstract
in as much as they attempt to classify a typology of outcomes within which
more specific achievements may be realized.
Collaboration Between the Disciplines as Process
Collaboration as process is simply the ways of working together. Its
constituent elements are contact, interaction, and communication. The
significance of process as a product is strongly associated with the notion of
action and response. Process is maintained by an open and constantly
maturing agenda, the development of which is supported by dialogue
between the disciplines. With regard to assessing the value of process, it is
difficult to judge collaboration as process as successful or unproductive
independent of the success of other, related products. However, as we are
concerned here with a specific collaborative endeavour, some indicators by
which we may begin to assess the constructive aspects of collaboration as
process can be suggested.
As a starting point, we may address the activities of the contact group
itself. The central dynamic that dictated the process of collaboration
between the modellers and sociologists evidently was the evolution of the
contact group. In terms of the number of people involved, the group
started with 10 members and completed its task with a core of just 3. In
terms of activities, the first contacts were in the form of formal, scheduled
meetings that gave way to informal visits between sites. Finally, the
frequency of interaction between members of the group increased as the
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project advanced. Hence the nature of process changed as a reflection of
the evolving interactions between individuals. But why change the nature
of the process? How did the group interactions evolve in this way . . . by
serendipity or design?
A partial response to this query is that it was just the way things
happened. Viewing the events in slightly more detail though, we can
pinpoint some of the reasons why the changes occurred. For example,
group size (the numbers of people actively engaged in the contact group)
became reduced in the latter stages of the project due largely to the fact
that as the model specification began to be narrowed down, fewer indi-
viduals were able to contribute in a constructive way. As the contact group
became smaller, informal modes of meeting and communicating became
the norm and the individuals involved got to know each other on a
personal basis. This informality was also reflected in the way progress was
measured. An explicit timetable linking specific activities with progress
was not evident at any time during the simulation project. Progress was
informally measured in terms of the relationship between assumed work-
load remaining and the various reporting deadlines for the project as a
whole. At one level of analysis (perhaps in terms of project management),
this is an undesirable mode of operation. There is, however, a sense in
which the process of collaboration was self-organizing in as much as the
group was able to formulate its own agenda and work programme to suit a
communal perception of progress.
As a closing comment with regard to the process of collaboration,
there is a lesson to be taken from the observed relationship between
disciplinary affinity and the structural level at which collaboration occurs.
This point was well demonstrated by the divergent approaches to collabor-
ative writing exhibited amongst the social science group (two anthro-
pologists and a sociologist). When composing joint written reports, the two
anthropologists were able to compose text almost on a sentence by
sentence basis, integrating their data and observations at the level of the
statement. In contrast, when the sociologist became involved, the focus
shifted to the paragraph or section and integration occurred through
thematic links. Although both sets of interactions were considered fruitful
by all parties, it is interesting to note that even a modest disciplinary
discontinuity can strongly influence the process of collaboration.
Collaboration Between the Disciplines as Understanding
When we understand, we take ownership of a piece of reality of which we
had been unaware. Our own reality is thereby enriched and at the same
time subtly changed. Collaboration as understanding is concerned with
this phenomenon. By prompting researchers from different disciplines to
work together towards some common aim, we are inviting them to enrich
each other’s reality. The hope (or design?) is that in combination they will
be able to describe a more representative and thereby useful picture of
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what we perceive to be a complex world. Collaboration without under-
standing is largely devoid of utility.
Although advice on how to understand and be understood effectively
is widespread, much of it is superficial and oriented towards instructing
and convincing rather than advancing comprehension. Within an intellec-
tual framework this distinction is crucial. A deep and comprehensive form
of understanding is required at the level of cross-disciplinary collaboration.
Because each individual researcher is positioned within a web of inter-
locking knowledge and associated meanings, the transfer of understanding
is best addressed at several levels. For example:
To understand the meaning of a sentence or whole discourse in an
argumentative context, one should not examine merely the words within
that discourse or the images in the speaker’s mind at the moment of the
utterance. One should also consider the positions which are being criti-
cised, or against which a justification is being mounted. Without knowing
these counter-positions, the argumentative meaning will be lost. (Billig,
1996: 91)
If understanding is to be a product of collaboration, it clearly has to be
recognized as a complex construct.
It is beyond the remit of this text to delve any deeper into the nature of
understanding between the disciplines. What we seek to suggest here is a
structured framework for discussing the issues surrounding collaboration
between the disciplines using evidence from a single project. However, we
would draw on our experiences of the project to emphasize two areas
where the relevant literature supports the available evidence. First, there is
an inertia inherent in all our understandings which was effectively de-
scribed by R.S. Wurman when he stated that: ‘we tend to perceive the
things that relate to our pre-existing interests and attitudes – either to
support or refute them. People have a tendency to shun or refute informa-
tion that contradicts these, whether consciously or not’ (Wurman, 1990).
Second, there are ways in which the effectiveness of collaboration as
understanding can be improved. The conceptual framework suggested by
Billig (1996) is again of interest here, as he suggests an analysis of what he
terms ‘witcraft’ or the skills of argumentation. For example: what is seen as
a persuasively structured argument? What style of presentation is effective?7
In the case study described earlier, it is debatable to what extent a common
understanding of the function of the model was essential to successful
implementation. Different disciplines will evaluate quality (of design and
function) in different ways, influenced by the epistemological, ontological
and teleological underpinnings of the fields of science to which they have
been exposed. Whilst we have no evidence that variation in such ‘epistemic
lifestyles’ (Shackley, 2000) inhibited communication and understanding,
we would suggest that their negative influence may not be so significant in
cases where there is no intra-disciplinary debate or where expertise and
ignorance can be more clearly delineated.
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Collaboration Between the Disciplines as Utility
Collaboration as utility relates to the explicit and implicit advantages that
emerge from collaboration and can be interpreted as the benefits or fruits
of the venture. These benefits may include other tasks such as under-
standing and negotiation, but they will also relate to the stated aims and
goals of the project as a whole.
Again, judgment of relative success is made difficult by the relation-
ships between this and the other benefits. Furthermore, utility accrues not
only to the project as a whole but also to the participants (researchers) and
others who have more indirect relationships with the study. However,
although utility clearly extends to encompass emergent features of the
work such as the experience of collaboration, there is a limit to how far any
interpretation of possible sources of utility can be justified. Simply put, a
comprehensive listing of sources of utility is problematic to identify, define
and measure.
The significance of collaboration as utility has relevance to the project
through the additional benefits gained over and above those that would
have accrued if the disciplines had remained intellectually isolated. A sub-
set of these benefits can be clearly identified:
• Emergence of a set of tools (including a common vocabulary and
dominant metaphors) which will make further collaborative work more
productive.
• A concern for some of the broader aspects of the problem set which, in
turn, are also of interest at a policy formulation level.
• Utilizing informed criticisms from members of other disciplines to
challenge accepted axioms and methodological issues.
• Development of a micro-simulation model which can be manipulated
as easily by the social scientists as by the computer modellers.
This form of utility only became evident in the closing stages of the
micro-simulation model development. By participating in the develop-
mental phases of the model, the social scientists were able to react to the
first demonstration simulations in a constructive way. Because they already
had a ‘feel’ for the structures and processes being represented, they were
able to contribute towards the refinement of the model’s performance.
Furthermore, the agenda for utilizing the model was unhindered by
queries concerning its validity and capabilities, as these types of issues had
previously been addressed to the satisfaction (negotiated consensus?) of all
parties. A significant ramification of this observation is that the model was
not, of itself, the final element in the modelling process. Data ownership
was not observed to be a significant point of conflict, perhaps because each
group was able to identify a stake in the model and its subsequent use. The
social scientists were to be involved in the model verification process which
entailed presenting the model’s output to a group of farmers in the field.
Dialogue between the disciplines (through query rather than negotiation)
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was prolonged far beyond the point at which it would normally be
observed.
Collaboration Between the Disciplines as Knowledge Integration
This final product of collaboration is in many ways a specific case of
collaboration as utility. First, we would mention that from a methodo-
logical standpoint, the various levels at which integration can be achieved
need to be distinguished. Three epistemological levels at which integration
can occur are clearly identifiable. ‘Data integration’ concerns the use of
combinations of raw facts and figures. ‘Information integration’ relates to
quantitative and/or qualitative data that have been analysed for trends, or
tested against hypotheses. Second, ‘knowledge integration’ occurs where a
synthesis of contextually homogeneous and epistemologically rigorous
information is achieved. This data–information–knowledge axis constitutes
a core procedural element of the scientific method and is no less significant
as a classification system for integrating the contributions of different
disciplinary researches. A significant feature of this classification is the
associated sets of activities that support the various levels of integration.
The most important of these will involve scoping how, when and in what
formats different types of data, information and knowledge will be gen-
erated through the collaboration. Failure to consider how different con-
tributions are to be formally coupled or integrated can be seen as one of
the causes behind criticisms of poor quality in cross-disciplinar y en-
deavour. Maintaining the excellence and integrity of disciplinary contribu-
tions is a prerequisite for credible, robust interdisciplinary science. Much
more work is needed to develop ‘mapping functions’ between qualitatively
different knowledges if the benefits of integrative science are to be
realized.
Although collaboration within the project took place at all three
methodological levels described earlier and between a diverse range of
cross-disciplinary groupings, there was little explicit debate concerning the
appropriate degree of integration. Rather, a number of regular and in-
sightful queries were voiced at various times during the project: should the
various contributions be merely coupled as a sequence of individual studies
that feed data and information onto the next activity, or coordinated as a
fully integrated piece of work? Can areas of synergy be identified a priori?
And how should the appropriate degree of integration be achieved: by the
researchers themselves or by a third party? Doubtless these questions are
best considered during the project design stages. However, they may not be
at the forefront of the researchers’ minds at this time due to the practicali-
ties of project design and budget constraints (that is, resources are very
often not available for a detailed examination of cross-disciplinary issues
during project preparation). In thinking about potential opportunities for
cross-disciplinary collaboration in terms of the queries posed earlier, the
concept of ‘seams of complementarity’ was found useful. The use of the
term ‘seam’ here is meant to convey the idea of commonality in two
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dimensions, one of which cuts across the disciplines, the other crossing
methodological levels (data, information, knowledge).
Seams of complementarity are areas of potential synergy between the
various disciplinary activities within a project. The potential for exploiting
these areas should be explored prior to embarking on the project’s work
plan. Whether the opportunity is concerned with data provision by a social
scientist for a computer modeller, provision of a predictive model by a
modeller for a social scientist or comparative surveys by different groups of
researchers, the possibilities need to be explored and decisions made
regarding the format and level of collaboration/integration required. Not
all the opportunities for collaboration are evident prior to project com-
mencement and occasions for collaboration emerge throughout the course
of the research, although some of these may be deleterious to the project as
a whole. Hence, collaboration between the disciplines is an evolving set of
opportunities, which requires management and supervision if it is to be
exploited for the overall benefit of the project.
Discussion and Conclusions
The evidence upon which conclusions concerning collaboration between
the disciplines can be based comes from observations and impressions of
the interactions between researchers involved in a specific project. Conclu-
sions based on this information should therefore be treated with some
discretion for three reasons. First, the study involved limited systematic
data collection in an empirical sense. No hypotheses were tested and no
theory examined. Second, and as noted in the introduction to this paper,
there has been no formal attempt to investigate or analyse the psycho-
logical or sociological aspects of collaboration. A lack of comment regard-
ing the significance of personality is perhaps this contribution’s most
serious deficiency, as the character and temperament of those individuals
involved in the project had a clear influence on the dynamics of cross-
disciplinary interaction (both in terms of the tools and the products of
collaboration). Third, the specificity of the study plainly limits the extent to
which we can draw generalizable inferences. The details of who was
involved in the project (in disciplinary, cultural, linguistic and status
terms), the objectives and the subject matter of the project, all serve to
constrain the authority of this study’s findings. Despite these caveats, there
are several interesting threads of evidence that contribute towards the
development of a richer picture of cross-disciplinary collaboration, and it
would be remiss if we were not to draw attention to three key points which
emerge from the study.
The first point we would make is that genuine collaboration between
the disciplines has utility in its own right. The development of a common
vocabulary and a style of dialogue (negotiation in this case), the recogni-
tion of significant elements of metaphor and the emergence of dominant
metaphors, and the acceptance by the different disciplines of an inter-
mediary, all require time and contact. Learning to manipulate the tools of
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collaboration constitutes a learning curve which collaborating researchers
explore together. However, both time and contact are valuable, and often
limited, resources within the framework of an interdisciplinary research
programme. Where such resources have been expended therefore, we
would suggest that there is an argument to be made for retaining the value
of the investment.
The experience of involvement in a genuinely cross-disciplinary dis-
course is clearly of benefit to the individual researcher in both educational
and career terms. However, the utility of such collaboration to those bodies
which commission research programmes has traditionally focused on the
benefits to policy formulation. Significantly less attention has been paid to
the experiential value which is contained within a cross-disciplinary re-
search group and which is lost when the members go their separate ways.
This should not be interpreted as an argument for retaining the same
research group across several research programmes simply because they
have worked together successfully in the past. Such an ill-advised strategy
would likely result in intellectual stagnation and the emergence of a ‘meta-
discipline’ formed from debased versions of the component disciplines.
Consequential loss of the diversity of scholastic contribution, which is such
a desirable feature of cross-disciplinar y work, would clearly be counter-
productive.
A more appropriate model would involve the maintenance of a core
team, the precise make-up of which might reflect the central thematic
components of the research programme and may include any intermedi-
aries whose contributions have been found useful during past studies. The
aim is not so much to maintain continuity of knowledge as to retain
continuity of experience. For example, we noted earlier that the determin-
ants of successful collaboration have as much to do with the personalities
involved as they have with the mechanics of common vocabularies or the
emergence of dominant metaphors. Hence, the latent potential for success-
ful collaboration is possessed by individuals in a form that defies transfer.
The particular characteristics of the tools of collaboration are specific to
each case, because they are a partial function of personal interaction.
Information relating to the use of tools in one case will not guarantee
successful use in another.
The second concluding point concerns the planning and resourcing of
cross-disciplinary activities. Previous experience with interdisciplinary
work has emphasized the point that substantive and meaningful cross-
disciplinary collaboration will not miraculously ‘emerge’ from either phys-
ical or intellectual proximity. The project described in this paper demon-
strated that, by explicitly planning for and managing interdisciplinary
interactions, a high degree of data, information and knowledge synthesis
can be achieved. Several lessons are evident. For example, defining the
products of collaboration in the project proposal and listing them in the
work plan will prevent collaboration being an ad hoc component of the
project, engaged in when time allows or inclination is aroused. Respons-
ibility for the products of collaboration should be clearly assigned and a
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specific budget allocated for achieving them. The nature and potential of
the ‘seams of complementarity’ described earlier need to be identified at an
early stage. Whilst it is beyond the scope of this text to hypothesize about
effective or efficient methods for identifying and exploiting seams of
complementarity, the direct evidence from the project suggests that early
formalization of the nature, level and mechanisms of collaboration is
beneficial.
Third, the observations made during this study have ramifications for
the training of researchers. The skills and knowledge required to operate
effectively not just within, but as a positive contributor to, a cross-
disciplinary research team are dissimilar to those required to function
effectively as a lone researcher or within a narrow disciplinary team. These
skills will enable researchers to: (1) integrate perspectives that come from
different paradigmatic, epistemological, and disciplinary traditions; (2)
generate abstract representations of phenomenological structures and pro-
cesses with which to explore problems; (3) communicate and interact
effectively with co-researchers from other disciplines. We suggest that these
skills would be promoted through competency in: (1) systems thinking;
(2) modelling (both conceptual and formal/symbolic); (3) written, verbal,
and visual communication.8Whilst these competencies will be required by
all researchers working in cross-disciplinary teams, we are also mindful of
the significant role identified during the study for managerial and coordin-
ation functions. Cross-disciplinary team leaders will also therefore require
an additional set of skills in structuring and managing the nature of
interaction between team members and in managing data, information, or
knowledge integration.
We note that if cross-disciplinary collaboration is a stated aim of a
research programme or project then sufficient resources need to be pro-
vided to support it. Again, making the products of collaboration a specific
element of the proposal and work plan will aid budget estimation and
provide an audit trail for the interdisciplinary aspects of the research.
Contrary to current funding trends, we would stress the benefits of a
separate budget element to support cross-disciplinary collaboration. Ex-
pecting collaboration to be funded out of the various disciplinary groups’
own budgets both undervalues (and in a way compromises) the utility of
interdisciplinary activities, and serves to promote the ad hoc approach to
collaboration criticized earlier.
Finally, we would emphasize the point that the unification of different
intellectual and academic fields is not simply a pragmatic alliance that
operates mechanically and predictably. On the contrary, the process of
creating new understandings provides opportunities for engaging in mean-
ingful debate about theory, methodology and technique, to the benefit of
all those involved. This article tentatively identified some of the tools of
collaboration that can be utilized to create new understandings. We have
also mentioned four specific products of collaboration and discussed their
meanings in terms of the project studies. As a parting observation, the
following simple piece of advice is offered: ‘As you learn about something
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try to remember what it is like not to know. This will add immeasurably to
your ability to explain things to other people’(Wurman, 1990: 130).
Notes
The work reported in this paper was funded by DG XII of the European Commission as
part of the ‘Environmental Perception and Policy Making: Cultural and Natural Heritage
and the Preser vation of Degradation-Sensitive Environments in Southern Europe’ project
(Contract no. EV5V-0486). The author would like to thank Roger Seaton, Nick Winder and
Sander van der Leeuw for comments on earlier drafts of the paper. The author is also
grateful for constr uctive comments provided by two anonymous reviewers.
1. Significant exceptions to this lack of innovation being the contributions of Judge (2002)
on the use of metaphors as vehicles for transdiciplinar y discourse and Romm (1998) on
the reflexive nature of interdisciplinary practice.
2. We would guide the reader to the substantive work being carried out on Conceptual
Integration (Turner & Fauconnier, 1995) and Cross-domain Meaning (Cummins, 1993)
as examples of contemporary developments in this field.
3. We would note that one limitation of many practical instances of collaboration between
the disciplines is that collaboration per se is seen as the goal without due attention being
given to its various dimensions.
4. A somewhat crude but nonetheless genuine representation of the significance of this
factor.
5. See for example the seminal collection of papers in Ortony (1993) or the work of Steiner
(1975). Note that analogy has been considered as a special case of metaphor (for
example, by Gentner & Jeziorski, 1993) and we will therefore use the latter term to
include both phenomena.
6. By indirectly here we indicate that whilst ‘complexity’ and how to manage it was never
an explicit issue, it was the underlying subject matter of other debates.
7. This framework can be seen to build on the distinctions made by Aristotle between
Logos, Ethos and Pathos.
8. The written element of this skill set will include non-language representations such as
diagrams and illustrations.
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Paul Jeffrey is a Senior Research Fellow at the School of Water Sciences,
Cranfield University, UK. Working at the interface between technology,
society and the environment, his research is focused on exploring how
water technology and policy options impact communities and the
environment. His work on cross-disciplinary research saw him nominated as
a finalist for the Swiss Academy of Science’s prize for Transdisciplinarity in
2000.
Address: School of Water Sciences, Cranfield University, Cranfield,
Bedfordshire MK43 0AL, UK; fax: +44 1234 751671; email:
p.j.jeffrey@cranfield.ac.uk
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