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The Challenges of Cross‐disciplinary
Research
Jens Aagaard‐Hansen
Published online: 05 Dec 2007.
To cite this article: Jens Aagaard‐Hansen (2007) The Challenges of Cross‐disciplinary
Research, Social Epistemology: A Journal of Knowledge, Culture and Policy, 21:4, 425-438, DOI:
10.1080/02691720701746540
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Social Epistemology
Vol. 21, No. 4, October–December 2007, pp. 425–438
ISSN 0269–1728 (print)/ISSN 1464–5297 (online) © 2007 Taylor & Francis
DOI: 10.1080/02691720701746540
The Challenges of Cross-disciplinary
Research
Jens Aagaard-HansenTaylor and FrancisTSEP_A_274611.sgm10.1080/02691720701746540Social Epistemology0269-1728 (print)/1464-5297 (online)Original Article2007Taylor & Francis214000000October–December 2007
During the past decades, research collaboration between researchers from different disci-
plines has become more frequent. However, there is a need to look into the generic modalities
and challenges. The article explores a series of potential obstructions to cross-disciplinary
collaboration of methodological and epistemological nature. Furthermore, a number of
contextual, inhibiting factors are outlined. As means of overcoming the obstacles, the
importance of mutual knowledge, allocation of adequate time and conducive research
management is emphasised. New teams may benefit from tutoring by facilitators, who can
help to make problem areas explicit and negotiate solutions. Owing to the training back-
ground of the author, most of the examples are drawn from the interface between biomed-
icine/naturalscienceandappliedmedicalanthropology.However,theissuesraisedbasically
apply to all sorts of cross-disciplinary research collaboration in various combinations.
Keywords: Applied Research; Biomedicine; Cross-disciplinarity; Epistemology;
Facilitators; Medical Anthropology; Methodology; Paradigms; Research Management
Introduction
Thetopicofthisarticleistheclusterofdifferencesthatshouldbeovercomeifresearchers
with different disciplinary background are to collaborate. It will be guided by the defi-
nitions of Rosenfield (1992, 1351), who ventured a taxonomy of the level of integration
between disciplines:
Level one: Multidisciplinary. Researchers work in parallel or sequentially from disciplinary-
specific base to address common problem.
Level two: Interdisciplinary. Researchers work jointly but still from disciplinary-specific
basis to address common problem.
Jens Aagaard-Hansen is a senior researcher with a double training background as medical doctor and anthropol-
ogist. For the past 14 years he has been based at DBL—Institute for Health Research and Development (formerly
Danish Bilharziasis Laboratory). He has been involved in cross-disciplinary research, applied medical anthropol-
ogy, research management and research capacity strengthening mainly in developing countries. Correspondence
to: Jens Aagaard-Hansen, DBL—Centre for Health Research and Development (formerly Danish Bilharziasis
Laboratory), Jaegersborg Allé 1D, DK-2920 Charlottenlund, Denmark. Email: jah@life.ku.dk
Downloadedby[NovoNordiskA/S]at06:5113March2015

426 J. Aagaard-Hansen
Level three: Transdisciplinary. Researchers work jointly using shared conceptual frame-
work drawing together disciplinary-specific theories, concepts, and approaches to address
common problem.
The general perception of and distinction between multidisciplinarity and interdiscipli-
narity is shared by a majority of relevant scholars (for example, Albrecht, Higginbotham
and Freeman 2001, 73; Friedman and Friedman 1985, 77; Gilbert 1998, 6; Kilburn 1990,
132 ; Kline 1995, 2; Maina-Ahlberg, Nordberg and Tomson 1997, 1230; McNeill 1999,
314). Although they phrase it in different ways and may even have slightly different
views, there is a general agreement that interdisciplinarity entails more integration than
multidisciplinarity. Even Rosenfield’s addition of transdisciplinarity is endorsed by
many (Albrecht, Higginbotham and Freeman 2001, 73; Maina-Ahlberg, Nordberg and
Tomson 1997, 1230; McNeill 1999, 314). In this article the term cross-disciplinarity will
be used according to Rosenfield (1992, 1351); that is, as a general designation for all the
three terms (multidisciplinarity, interdisciplinarity and transdisciplinarity). Aagaard-
Hansen and Ouma draw attention to the time dimension in the sense that cross-disci-
plinarity “is a gradual process in which the research group little by little moves in the
direction of integration—from multi- to transdisciplinarity and which is taking place
at different paces” (2002, 206).
There are basically two motives for cross-disciplinary research, either to provide
results that can be applied or as a source of inspiration for new, overarching research
questions and exchange of methods and conceptual frameworks (Aagaard-Hansen
2003, 32–36).
When the extent of integration of disciplines is considered, it also plays a role how
different they are. McNeill explains “the nature of both bridge-building and restructur-
ing will differ according to what I call the scope that is, the number of disciplines
involved and the distance between them” (1999, 314). He continues, “the distance
between disciplines can in some cases appear small, in terms of subject matter, but
be large in terms of methodology or perspective” (McNeill 1999, 315). Based on the
training background of the author, the examples used in this article are derived from
collaboration between anthropology and biomedicine. However, the issues raised are
of a general nature and are to various extents relevant to interaction between other
disciplines as well. Many of the points are hardly new as such, but are brought together
here in an overview that may be of use to research teams who venture into the field or
programme managers who want to commission research of this kind. The article is
making its points by setting up a series of dichotomies. Evidently, this is an oversimpli-
fication of the many ongoing, cross-disciplinary projects that are rather positioned at
continua between the extremes.1
However, there are two justifications for this: one is
didactic; the other is that a certain proportion of our colleagues are still bound by these
polarised positions.
Challenges for Cross-disciplinary Research
However good the intentions, cross-disciplinarity is difficult to operationalise. In the
process, researchers may face many situations in which collaboration is impeded

Social Epistemology 427
either by lack of knowledge, divergent standards, different approaches, or simply
negative attitudes and prejudices. Below is a brief outline of the main areas in which
problems may arise. The issues are intimately interlinked, but described separately for
the sake of clarity.
Quantitative Versus Qualitative Methods
The difference in data collection methods is usually the most conspicuous, and
consequently the first, issue that comes to mind. For scientists having been socialised
within particular discourses, it can be rather difficult to come to terms with alternative
approaches. Either figures or big chunks of text and observation notes may be consid-
ered the only appropriate way of representing the reality. In the process of establishing
a cross-disciplinary team it is necessary to reach an initial, common understanding that
the data collection method of choice is directly derived from the character of the
research question and that no method is better than any other per se. However, if the
time and funds allow, it is possible to get the best of both worlds by combined use of
quantitative and qualitative data collection methods.
Closed Versus Open Approach
The choice of data collection methods often has a direct bearing on the degree of open-
ness. If the aim is to end up with a concise figure describing a certain occurrence
(combination of variables), a closed approach is needed—irrespective of whether it is
the measurement of a chemical substance or choices between pre-defined responses to
distinct questions in a questionnaire. If the idea is to obtain a balanced picture of a
phenomenon as it is seen through the eyes of an individual, a more open approach is
suitable. Both are appropriate under certain circumstances—the former when it is
important to explore tangible, quantified correlations between different variables; the
latter when the main aim is to provide a multi-faceted, thick description of the new
field. The former is necessarily based on the researchers’ preconceived categorisation;
the latter on trying to avoid exactly that and letting the categories emerge from the data.
Both are valid approaches depending on the circumstances.
However, there is another level at which the degree of openness is relevant—the level
of study design. It is considered a virtue for many biomedical studies that as much as
possible is planned in advance (e.g. specific research questions, study populations,
sampling procedures, design, exclusion criteria and data collection methods), in order
to be able to control bias and confounders influencing the outcomes under study.
Although the same applies to a certain extent to anthropological studies as well, it is not
quite as rigid. A change of focus, study population or data collection method halfway
through a study would usually mean a major threat to a biomedical trial, whereas it
would be more legitimate for an anthropologist. In fact it is considered a virtue for an
anthropologist to be receptive to unforeseen events in the field and adapt accordingly,
and it does not necessarily jeopardise the study per se. As with data collection methods
there are no good or bad, but simply more or less appropriate, designs.

Objectivism Versus Subjectivism
The issue of openness described above is linked to one of the predominant sets of prej-
udices. The fact that anthropologists function as their own data collection instruments
and that they have more degrees of freedom when it comes to introducing changes in
the research process makes some biomedical colleagues wonder about the objectivism
of the data produced. Where is the borderline between scientific findings and the
researcher’s personal opinions? The opposite position constitutes scepticism towards
the biomedical science’s claim for objectivity. Whereas the archetypal, medical stand is
a quest for objectivism based on traditional positivistic virtues, anthropologists see
subjectivism as a basic condition for all research and that anthropology is based on a
process of inter-subjectivity (Jackson 1998; Schutz and Luckmann 1974). In brief, this
refers to a scientific position that acknowledges subjectivism is a basic precondition for
research per se. Thus, the aim should not be to eliminate subjectivism, but to take it into
considerationasacharacteristicoftheprocessofdataproductioninvolvingtwosubjects.
Causality Versus Description
Much outstanding research has simply aimed at describing phenomena as they are; for
example, descriptive, epidemiological studies of disease patterns in a given population
or cultural studies of perceptions and practices in a certain ethnic group. Other studies
have had the aim of illuminating a specific causal relationship or a cluster of the same;
for example, whether a high level of education makes women have other treatment-
seeking practices for their infants than women with a low educational level. Notwith-
standing the importance of both ventures, this is an area where the two scientific
discourses often differ. Hence, it is more legitimate to conduct non-causality studies
within anthropology than it is within biomedical science. A research question that does
not deal with causal relationships is often considered a bit shallow by medical scientists,
whereas it is the rule rather than the exception for anthropologists.
Apart from the implicit attitudes attached to these positions, there is a much more
tangible consequence as well. The nature of such a research question has major conse-
quences for the design. The quest to answer the specific causality type of questions
necessitates a certain sample size, sampling procedure, choice of closed data collection
method and an appropriate study design.2 This is related to the section on closed versus
open approach above.
Text Versus Context
There is another dichotomy that characterises the two scientific discourses: the focus
on either text or context. The terms, which are derived from linguistics, refer to the text
(a phenomenon, a sign, a sentence, an item, an act) and the context (the setting, the
socio-cultural environment, the biotope in which the text is situated). Put simply,
biomedical research focuses on the text and tries to control context as a source of noise
(confounders) that threatens to disrupt the research. It can be the aim to study a certain

dependent variable (the growth rate of bacteria or incidence of a disease) by controlling
every factor in the environment (the media and temperature or other potential risk
factors) except from the independent variable in question. In contrast, anthropologists
make context a main area of research in its own right. Context in its multiplicity is stud-
ied as a necessary part of a given phenomenon. Only by seeing the variations of the text
in the light of the complexity of and changes in the context can a valid picture of socio-
cultural issues be obtained. Among these two basic positions, one is not superior to the
other per se. But as is the case for data collection methods, one or the other is preferable
in order to conduct good research on different kinds of research questions. However,
if this basic difference in approach is not realised, it can cause major problems in cross-
disciplinary teams.
Absolute Versus Relative Perspective
Research is conducted in order to provide new knowledge. Some (more positivistic
inclined)scientiststakeitforgrantedthatthereisatruthoutthere,andthatthechallenge
is to get as close as possible to it by conducting good research. Other (more phenome-
nologically inclined) colleagues do not see their aim to be a search for a golden truth,
but rather a quest for providing valid representations of local lifeworlds (Schutz and
Luckmann 1974, 59–92). Whereas the biomedical researchers explore universal causal
relationships (e.g. smoking as a cause for lung cancer), the anthropologists have the
ambition of describing the world as seen through the eyes of the particular study
population in question (i.e. if witchcraft is considered an important cause of misfortune
in a certain ethnic group, the challenge for the anthropologist is to describe that in a
fair manner—not to evaluate whether it is real or not). It is crucial that this anthropo-
logicalendeavourisnotlookeddownuponbybiomedicalcolleagues,butisappreciated,
partly because it is based on sound scientific (but different) ground and partly because
it is exactly the source of the potential usefulness of anthropology to public health. On
the other hand, anthropologists have to realise that there is a need for closed and rigidly
designed studies in order to provide knowledge about the effect of various interventions
without a number of confounding factors. This divide is often expressed as the etic
(global scientific standard, in this case biomedicine) versus the emic (local, indigenous
view as described by the anthropologists) position (Pike 1971).
Representativity and Validity
The issue of representativity plays a major role in most public health research. It is the
issue of whether the study sample (e.g. 200 people from which a specific blood sample
has been taken) share the same characteristics as the larger population about which
inferences are made (e.g. the people of Finland). Only if the sample and the popula-
tion have the same proportions of all relevant variables (such as sex, age, ethnicity,
class or whatever else plays a role with regard to the research question) are broader
conclusions justified beyond the borders of the sample. The issue of representativity
should be considered already in the planning phase.3
In this article, validity is defined

as “the extent to which a study finding reflects a real fact of life”.4
In this broad defini-
tion, many things can contribute to reduced validity. For instance various sorts of
systematic error can play a role, such as selection bias (i.e. skewness as a result of
mismatch between sample and population) or information bias (i.e. the phenomenon
that the source of information—a thermometer or an interview—does not give a true
picture of the phenomenon the researcher wants to study). But it can also be caused
by random error, which is a reflection of natural variation in data collection methods
(and hence imprecise data). Whatever the case, a high degree of validity is the aim of
every researcher.
Usually anthropological studies deal with relatively few informants, with whom the
researcher interacts for long periods of time typically during series of in-depth
interviews and observations. The in-depth data collection takes place at the expense
of having a relatively small sample size, and is consequently more exposed to be influ-
enced by individual variation among the informants. On the other hand, biomedical
studies, however complex they may be, typically take relatively simpler measures (e.g.
blood samples, questions, weight, etc.) from each individual, which permits such
studies to have large sample sizes. Thus, an anthropological study will tend to provide
very valid data (exactly because the researcher has interacted with the same infor-
mants for a long time) whereby more hidden and sensitive issues are more likely to
come to the fore and be seen in changing contexts, whereas the standardised way in
which biomedical data are collected does not offer the same opportunity. In short,
anthropological studies are typically relatively weak when it comes to representativity
and are strong in validity, whereas the opposite is often the case for biomedical
studies. Again one is not better than the other per se, but is simply suitable for differ-
ent purposes. As with data collection methods, it is here possible to design studies
that are strong in both aspects if resources allows.
Role of Theory
All scientific disciplines are grounded in theoretical foundations and paradigms.
Biomedicine has developed a tightly knit set of anatomical, physiological, genetic and
biochemical models that have proved extremely useful when it comes to explaining
disease processes. Apart from a few philosophically inclined public health scientists,
these theories and models are taken for granted as absolute, fixed points in the episte-
mological universe. In contrast to this stance, the average anthropologist spends quite
a bit of time discussing the theories on which studies are based. The extent to which
theoretical issues are of concern is a major difference between the two disciplines and
a potential source of prejudices. The anthropologists may be perceived as wordy people
making a lot of fuss about nothing, whereas the opposite prejudice is that biomedical
researchers are turning a blind eye to the shaky, epistemological ground of their own
endeavour. For the anthropologists working in the applied field, this poses a particular
challenge. Some claim that it is not possible to live up to the academic, disciplinary
demands of dealing adequately with theoretical issues and being applied at the same
time. It is indeed difficult, but not impossible.

Research Ethics
Biomedicine has a tradition for detailed ethical codes whereas some anthropological
guidelines are held in more generalised terms (Aagaard-Hansen, Johansen and Riis
2004). For instance, the key element of informed consent is dealt with much more
explicitly by the biomedical codes (Fluehr-Lobban 2003). This is not to say that some
disciplines are unethical. However, when disciplines are to collaborate it may take some
effort to harmonise the approaches to ethical issues as well.
Terminology
Many of the methodological and epistemological differences are expressed in different
terms. In the cases where there are distinct jargons, this may pose an additional hurdle
to overcome. More subtly, however, there are sometimes varying meanings of the same
terms within different disciplinary discourses. McNeill, García-Godos and Gjerdåker
also point to language variations in different disciplines as a potential barrier, and say
that “sometimes there appears to be common understanding and it is not for some time
that serious divergence in understanding is revealed” (2001, 17). Svedin et al. (1999)
point out terminology as an important element of assessing cross-disciplinary projects.
Are key concepts used consistently within the “project language”? The relation to the
use of facilitators (see below) is evident.
Power Balance
As anything else in life, research is influenced by power and status. Research projects
can be seen as the potential battlefield of individuals as well as disciplines. Historically,
some disciplines can be perceived as being more powerful than others—either because
of access to funds or simply due to status. Disciplinary boundaries are often propped
up by attitudes, the origins of which are rarely rational. A strong influence comes from
the positivist philosophy, which was launched by Auguste Comte (1798–1857) and
which still frames the thinking of many. As expressed by Kline, “the hierarchy that
Comte suggested went in descending order: math, astronomy, physics, chemistry,
biology (including physiology), sociology” (1995, 208). Another view on the prestige
heldbyvariousbranchesofresearch,accordingtoFriedmanandFriedman,isthat“there
is an informal hierarchy of disciplines and fields in academe in which the more presti-
gious theoretical basic disciplines regard scholarly interaction with colleagues in the
applied, less theoretical fields as an unrewarding experience” (1985, 79). Traditionally,
biomedicine has had a stronger position than social science. In cases where disciplines
have collaborated, the biomedical researchers have typically been the principal investi-
gators whereas the social scientists have been invited to solve specific problems, such as
mobilisationofthestudypopulationorprovisionofdataoncertainconfoundingfactors
suchassocio-economicbackground.Aagaard-HansenandOumaemphasisetheimpor-
tance of the balance between the disciplines, a notion that encompassed two elements;
firstly the methodological differences between the participating disciplines (“how to do

research”), and secondly the power balance (“who decides what to study”). The authors
state that “managing an interdisciplinary project in such a way that it strikes a balance
between common reference to main objectives and disciplinary freedom is maybe one
of the main challenges to research managers in positions like ours” (Aagaard-Hansen
and Ouma 2002, 203).5
Bridging the Gap
The many potential challenges listed above may be overcome. In this section, some
practical remedies are outlined.
Mutual Knowledge
An obvious first step when cross-disciplinary teams are to succeed is to obtain mutual
knowledge. Insight into the basics of methodology, theories, epistemological and
historical aspects of the others’ disciplinary discourse (as have been described in detail
above) is essential for understanding and respecting the position of collaborators from
other fields. Conceptual compatibility is the basis for understanding and overcoming
negative prejudices and creating respect. Furthermore, knowledge about alternatives
gives a healthy perspective on one’s own habitual thinking, which is quite often taken
for granted.
Adequate Time
Several groups emphasise the need for a long-time perspective for cross-disciplinarity
to succeed (McNeill, García-Godos and Gjerdåker 2001, 26; Silva, Peeters and Lewis
1994, 184). Rosenfield expresses it as follows: “with continuing support to the same
team over a sufficiently long period of time and covering several types of problems, it
is more likely that disciplinary barriers can be transcended and increased understanding
and confidence about the value of other disciplines can be achieved” (1992, 1345).
Allwood and Bärmark state it more daringly: “one lesson to be learned from this is that
it might take a research project or two in order for a group of researchers to develop the
level of competence necessary for an interdisciplinary generation of problems” (1999,
78). Hence, researchers as well as funding agencies should allocate additional time for
the collaboration to succeed.
Conducive Research Management
The topic of research management is to a large extent neglected. If research manage-
ment as such is difficult, management of cross-disciplinary research is even more
challenging because of its complex nature and the particular problems it poses. The
ability of the team leader to create a conducive working environment in which
synergism can blossom between the disciplines is crucial. In their book on “mode 2
knowledge production”, Gibbons et al. point out the challenge of the “management

of disciplinary identities in transdisciplinary settings and the development of trans-
disciplinary capacities” (1994, 138–139). The importance of the human factor for
cross-disciplinary research has been explored by Gold and Gold (1983, 86),
Øvretveit (1993, 139–158) and Allwood and Bärmark (1999, 79). Aagaard-Hansen
and Ouma (2002) have analysed various aspects of management in relation to cross-
disciplinarity.
Facilitators
Severalauthorsrecommendthattheintricateprocessofestablishingacross-disciplinary
team and developing a proposal should be facilitated by an individual with adequate
skills. Gold and Gold recommend that “the magnitude of the burden depends upon the
number of different disciplines, the type and degree of their differences, and upon
the depth of integration required. When this burden is especially great, the network
(i.e. the group) may benefit from including individuals whose primary function is inte-
gration and whose specialised training deals with integrative methodologies (e.g. system
science)” (1983, 87). Such an individual has been termed “generalist” (Bärmark and
Wallén 1980, 231) or “bridge-scientist” (Epton, Payne and Pearson 1983, 41). In the
present article, the term facilitator will be preferred.
Some authors indicate more specifically what the role of such a facilitator might be.
Gilbert indicates the potential role of a facilitator, stating that “while that individual
may not have the disciplinary depth of any of the other members of the group, that
person will be able to translate between the other experts and can serve as a catalyst
for the development of the vocabulary and communication skills necessary for the
group to move from the production of multidisciplinary output to interdisciplinary
knowledge” (i.e. in the direction of increased integration) (Gilbert 1998, 10). Sharp
states that “the ideal bridge-scientist must have acquired a familiarity with a number
of sciences or technologies, their basic paradigms, methodologies, and ways of work-
ing; moreover, he or she must be sensitive to the motivations, aspirations, and value
orientations of people in the several disciplines” (1983 cited in Epton, Payne and
Pearson 1983, 19). McNeill, García-Godos and Gjerdåker point out the challenge of
establishing a common language and common standards, and suggest that “social
sciences could have this ‘glue’ function both at an epistemological level and a practical
level” (2001, 13–14). This concurs with the importance of common terminology
described above.
Contextualising the Cross-disciplinary Gap
Until now the article has dealt with the potential obstacles that may exist between
researchers of different disciplines, and some preconditions for how they may be over-
come. However, in addition to these intra-project challenges, there are a number of
contextual factors at play—factors that are beyond the control of the individual
researcher or project, but nevertheless have a strong (and usually negative) influence
on cross-disciplinary endeavours.

Organisation of Institutions of Higher Education
Several authors point to the need for structural changes at the universities in order to
facilitate cross-disciplinarity (Chen 1969, 353; McNeill 1999, 312; Silva, Peeters and
Lewis 1994, 184). Others concentrate more on the technical, organisational patterns
(Epton, Payne and Pearson 1983, 22–25). Hull (1994) provides a very illuminating
example from Indonesia of how institutional structures can constrain collaboration in
general, and cross-disciplinarity in particular. Gibbons et al., who deal with the issue of
cross-disciplinarity within the discourse of mode 2, have this to say about the hetero-
geneity and organisational diversity: “Over time, knowledge production moves
increasingly away from traditional disciplinary activity into new societal contexts. … in
Mode 2 research groups are less firmly institutionalised; … Though problems may be
transient and groups short-lived, the organisation and communication pattern persists
as a matrix from which further groups and networks, dedicated to different problems,
will be formed” (Gibbons et al. 1994, 6). The way institutions of higher education are
organised is of course closely related to the issue of learning cross-disciplinarity. How
should students be introduced to the potential and modalities of cross-disciplinarity?
Evaluation Procedures
Evaluationisanintegratedelementofresearch,thestandardassessmentprocedurebeing
peerreview.“Thecompetitivenatureofacademiclifeisafunctionoftheemphasisplaced
ongainingaprofessionalreputation…”(BecherandTrowler2001,118),“andthepeers
are the judges” (Becher and Trowler 2001, 86). However, the fairness of the principle
of peer reviews has been questioned when it comes to cross-disciplinarity (Epton, Payne
and Pearson 1983, 44; Porter and Rossini 1985). Hence, there is a need to take a critical
look at the criteria for evaluating cross-disciplinary research (Friedman and Friedman
1985, 81), both with regard to project applications and publications (see below).
Funding Agencies
The priorities of the calls for proposals provide forceful incentives for the formulation
of research questions. McNeill, García-Godos and Gjerdåker call cross-disciplinary
research “a high risk/high return endeavour” (2001, 8). Hence, the sponsors should
not only proactively encourage cross-disciplinary research, but also be willing to
allocate more time and more funds to such kind of projects. In addition, the funding
agencies should have access to appropriate expertise when it comes to evaluating
cross-disciplinary research applications.
Career Paths
The standard pathways laid down for academic careers are mainly confined within
disciplines. Researchers are accumulating credit and constructing networks within
their own discipline that help them in their careers. Accordingly, there are very few

positions (e.g. professorships) that are cross-disciplinary by nature. Trespassing into
collaboration with other disciplines does not usually promote the progress. It may be
seen as waste of time at best, or sometimes directly negative. Silva, Peeters and Lewis
(1994, 184) and Rosenfield (1992, 1353) provide examples from the public health field
in developing countries.
Publication Traditions
McNeill, García-Godos and Gjerdåker (2001, 34) state that publication of cross-
disciplinary research is often ascribed low status; and Epton, Payne and Pearson go
further by stating that this ‘is not surprising if one considers that most of the highest
quality journals are mono-disciplinary and editors are probably biased against cross-
disciplinary papers’ (1983, 44). Birnbaum, who ventured a very quantitative investiga-
tion of the success of Research and Development teams, reached a similar conclusion
in the sense that “the tight integration of activities, usually associated with interdisci-
plinary research, is negatively related to article production” (Birnbaum 1983, 57). This
may to some extent reflect the epistemological and methodological differences
outlined above. Becher and Trowler point out the different media preferred for
publication within the various disciplines. For instance, there are marked differences
between disciplines that value book publications (e.g. history) and articles (e.g. phys-
ics), or even more recent electronic preprints and on-line discussion groups, modalities
that evidently have major implications for the speed of information exchange (Becher
and Trowler 2001, 108–114).
Intra-disciplinary Differences
For obvious reasons, the focus of the present article is predominantly on the interac-
tions between the disciplines. However, according to the findings of Aagaard-Hansen
and Ouma (2002), the intra-disciplinary differences are also significant (for instance,
between colleagues at different continents or from different language groups)—an
issue that indirectly has a bearing on the cross-disciplinary processes within a given
project. The process of developing intra-disciplinary collaboration is influenced by the
time factor in the same way as cross-disciplinary research (Aagaard-Hansen and Ouma
2002; Suda and Aagaard-Hansen 2003). Becher and Trowler have dealt with the intra-
disciplinary diversity as well (2001, 43–44).
Conclusion
The article has explored aspects of cross-disciplinary research collaboration. Issues such
as the choice of data collection methods and design (quantitative versus qualitative and
closed versus open approach) may be sources of divergence. The researchers’ perceptions
of causality versus descriptive studies and representativity and validity should likewise be
sorted out in cross-disciplinary teams. The attitudes towards the absolute versus relative
perspective and objectivism versus subjectivism are also important. Crucial differences

may be derived from different emphasis on text versus context, role of theory and the
varying approaches to research ethics. Finally, the terminology and the power balance
within the cross-disciplinary team are of outmost importance for a successful result.
Mutual knowledge (conceptual compatibility) is a necessary first step towards smooth
cross-disciplinary collaboration. Hence, researchers working in cross-disciplinary
teams should have a certain minimum of knowledge about the other participating disci-
plines as a means to overcome prejudices and discover possibilities. The importance
of allocating adequate time should be emphasised. The way research management is
handled is crucial to the success of cross-disciplinary team work. In order not to rein-
vent the wheel, new cross-disciplinary teams are well advised to consult with someone
who has previous experience. So-called facilitators can assist the initial negotiations and
ensure that the necessary discussion and definition are addressed and clarified from the
start, so that implicit conflicts and misunderstandings are not continuously hampering
the collaboration.
Separate but nevertheless important are the contextual factors (organisation of
institutions of higher education, evaluation procedures, funding agencies, career paths,
publication traditions and intra-disciplinary differences) that are often serving as obsta-
cles and disincentives to cross-disciplinary work.
In spite of the complexity, there is a need for more researchers to enter this field. The
call for relevant and innovative, cross-disciplinary research makes it necessary. There
aremanychallengestocross-disciplinaryresearch,whichhavebeenoutlinedabove.The
examples have been drawn from the collaboration between biomedicine and anthro-
pology, and have been set up in a simplified and polarised way. However, I contend that
the issues are of relevance to other cross-disciplinary collaborations as well, although
their relative importance will vary depending on the specific disciplines involved. The
challenges should be carefully dealt with in the initial project planning phase.
Acknowledgements
ThankstothemanyresearchersatDBL—CentreforHealthResearchandDevelopment,
theInstituteofAnthropology(UniversityofCopenhagen),theInstituteofAfricanStud-
ies (University of Nairobi), the Division of Vector Borne Diseases (Ministry of Health,
Kenya) and the Danish University of Education (Copenhagen) and other research insti-
tutions, amongst whom I learned the practical steps of cross-disciplinarity.
Notes
1
[1] The article is based on another over-simplification. Medicine is presented as a well-defined
and homogeneous entity. However, medicine is in itself a complex discipline with strong
elements of natural science based on a positivistic paradigm as well as the public health
elements, many of which are strongly influenced by social science and which refer more to
hermeneutic traditions. In this article, the term biomedicine is used to emphasise the natural
science aspects.
2
[2] In addition to this, there is of course the often-committed sin of public health research to
draw causal conclusion based on data obtained from cross-sectional studies.

3
[3] Unfortunately this does not always take place. Either it may only be realised during the imple-
mentation of the study that there is a confounding background variable that was not considered
in the original design, and that weakens the study, or, even worse, it is never found out, with
the consequence that conclusions are wrongly believed to be true. In either case, such selection
bias should rightly be feared as a major threat to good research.
4
[4] The terminological details and subdivisions of validity are very complex and beyond the scope
of this article.
5
[5] In relation to this, the issue of the study population’s participation in the various phases of the
research process should also be considered. Thus, some may see the real issue not to be the
power balance within the research team, but that between the team and the study population.
However, this discussion is beyond the scope of the present article.
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