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Part V
Methodological Perspectives
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12
Interdisciplinarity in Action
Sophia Efstathiou and Zara Mirmalek
Interdisciplinarity:You are in the midst of it—or soon will be. Whether in
an interdisciplinary programme or not, you will find yourself working with
people who view problems from different angles than you and use different
tools to solve them. You will be in conversation with academics, industry, or
government collaborators with special approaches to identifying, analysing,
or evaluating issues. Mutual respect is needed but not sufficient for such col-
laborations to flourish.
This chapter gives you some keys for meeting the challenges of interdisci-
plinary work. We look at interdisciplinarity in action:how its challenges and
rewards play out in practice. Social science is especially touched by interdis-
ciplinarity as addressing social science issues often relies on the collaboration
of many different experts in academia, government, and policy. Indeed our
approach in writing this chapter is ‘interdisciplinary’ and offers a perspective
from the field of ‘Science Studies’.
Science studies, or science and technology and society (STS), joins social
science approaches with philosophy and history to study the production
of scientific knowledge about the world. Philosophical questions about sci-
ence examine ethics, methods, and deeper assumptions; history examines
the dynamics of change in scientific practice; social and cultural approaches
include analysis of social structures and power relations shaping and shaped
by science. STS brings together such questions and methods in an array of
studies of how scientists work together and cooperate with government and
industry; cases that provide information and ideas about the common prob-
lems you face in an interdisciplinary workplace.
Here we select some examples from our own work and others’ to focus on
three challenges:figuring the ‘whats’, the ‘hows’, and the ‘whys’ of interdisci-
plinary work. The first case examines how dealing with racial health disparities
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relies on deciding what is at stake in this complex phenomenon. The second
case discusses how responding to population ageing involves coordinating
how people work, in real time. The last section shows that doing ‘good’ inter-
disciplinary science involves combining different reasons why to pursue this
work. But before we go on, what is it to be ‘interdisciplinary’?
1. Discipline, Doctrine, Ethos:Interdisciplinary How, What
and Why?
Interdisciplinarity is an approach to academic learning, research, and teaching
that is juxtaposed to mono-disciplinarity and to multidisciplinarity. The term
‘discipline’ is commonly used to define the academic backgrounds or learned
skills of professionals. Discipline comes from the Latin discere, ‘to learn’. In the
Catholic Church discipline outlined the rules one had to follow to behave in
the ways desirable, while doctrine corresponded to the theory or belief guid-
ing and realized by discipline (Cunningham 2002). Doctrine was what a disci-
ple (a student) learned from a doctor (a teacher) through discipline.
Universities began their existence as places where Catholic monks and nuns
were educated, so it is no surprise that we still call the training of university
students into various professions ‘disciplines’ and give our experts ‘doctor-
ates’. The thing to remember is that disciplines are fundamentally practices
with rules and methods for imparting doctrine, applied and upheld with a cer-
tain rigour or force—for instance examinations, and penalties for failing these.
Multidisciplinarity involves many disciplines working side by side, each
on the questions it is expert on, with a strict division of labour for address-
ing different aspects of a challenge. Interdisciplinarity, on the other hand,
involves a stronger form of disciplinary mixing. Consider the etymology of
the connective ‘inter-’ which means ‘in between’ or ‘among’, as in interna-
tional, or ‘mutually’ as in interrelated, or interacting. This is strictly speaking
different from multidisciplinarity, which would rather involve ‘multi-’, many
or multiple, disciplines, but would not necessarily mix them (Alvargonzález
2011; Klein 2010).
Interdisciplinarity must by default involve some sharing or mutuality in
disciplinary practice and theory, shaping joint questions and joint answers
to them. Such an integration of disciplines could lead to the development of
new questions and domains of practice.
This brings up the question of interdisciplinary ethos. Ethos comes from
Greek, where it has a dual meaning:‘habit’ or ‘custom’, and ‘character’. Ethos
comprises habits that build and define (moral) character. Character, also a
Greek word, comes from the verb charaso, which means ‘to carve’. Our char-
acter is built from habits, repeatedly carving us out.
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When we examine the ethos of science, or do ‘science ethics’ we are
asking:how do our science habits and practices carve out people, and the
world? Different disciplines, different practices, carve out the world dif-
ferently. They have and form different characters for people, and for the
research produced. As we see in Chapter9, the visible moral character of
research, what ‘good’ it drives to uphold or attain, can motivate people to
become part of a discipline. The ethos of an interdisciplinary effort is thus
also up for mutual negotiation and formation, along with its ways of rais-
ing and addressing questions.
Using these distinctions between discipline, doctrine, and ethos we can
think of being interdisciplinary as responding to three main challenges:
• Finding a common basis for understanding what is at stake (doctrine)
• Deciding and coordinating how to deal with issues at stake (discipline)
• Justifying why we should pursue shared research on these matters
(ethos)
The first challenge is sharing a starting point from which to develop
shared understanding and knowledge of an issue. It includes figuring out
the nature(s) of problems that call for collective work precisely enough to
join available expertise, develop it, and put it to use. The second challenge
concerns how we work, how we make work communal, and communicate;
what kinds of routines we need to coordinate, what kinds of structures and
infrastructures we need to support work of different kinds, and with what
rhythms and tools to mesh these. The third challenge is motivating and
justifying interdisciplinary work that escapes well-defined fields of exper-
tise, while respecting existing drives and skills that inspire committed,
well-informed work. These three aspects of interdisciplinary work, what it
is about, how it is done, and why it is done, are interrelated and they are
not fixed once and for all:‘Whats’, ‘hows’, and ‘whys’ all get negotiated
and renegotiated, through collaborative, interdisciplinary work. We next
see how.
2. Challenges in Action
2.1 First Challenge:What is at Stake?
Brought together to handle a pressing social issue, one of the first challenges
collaborators face is understanding exactly what is at stake. How is a stated
issue interpreted by each collaborator? One outcome of disciplinary training
is that different disciplines tend to see the world differently. They specialize in
studying different phenomena or different aspects of the same phenomenon.
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One case of disagreement about what is at stake is the discussion of ‘racial
health disparities’ in the United States. ‘Racial health disparities’ are differ-
ences measured in the average health outcomes of people from different
‘race/ethnicity’ groups in the US. For example, ‘Black’ adults are 40 per cent
more likely to be diagnosed with diabetes than ‘non-Hispanic White’ adults,
71 per cent of people with HIV/AIDS reported in 2010 belonged to racial and
ethnic minorities, tuberculosis was diagnosed 8.5 times more in ‘Blacks’ than
in ‘Whites’ in 2007, and ‘Black’ infants are three times more likely to die
of complications with low birth weight than ‘non-Hispanic White’ infants.
Because of insurance and geography people identified with particular catego-
ries of ‘race’ and ethnicity have a harder time getting health care, and when
they do get care, it is on average of poorer quality than health care given to
‘non-Hispanic Whites’.
Money, effort, and concern are invested in addressing ‘racial health dis-
parities’:one reason why the US instituted in 2010 a National Institute of
Minority Health and Health Disparities (NIMHD). As the Director of the
National Institutes of Health (NIH), Francis Collins,says:
This change by Congress reflects the importance of studying the issue of health
disparities with an even greater intensity. We need to learn much more about
what causes disparities—including the role of society, the environment, and
genes—and to find effective ways of overcoming or changing them.1
Director Collins sets out a problem and what matters for solving it. He says
that researchers need to figure out the causes of these disparities and change
them. Where should they look? Society, the environment, and genes. Like
with other complex challenges, this one cuts across more than one field of
expertise:social, environmental, and genetic expertise.
Health experts might agree on the challenge, broadly put:how can we get
everyone to have good health, irrespective of their ‘race’ or ethnicity? Yet
exactly what is at stake here is not so clear. Health scientists trained in the
social sciences tend to see social and economic solutions to this problem,
whereas scientists with training in genetics and molecular biology are pursu-
ing pharmaceutical and pharmacogenetic solutions.
Experts in social sciences, including law, sociology, demography, geron-
tology, psychology, public health, and social epidemiology reason as fol-
lows:money and educational resources are distributed differently across
‘race/ethnicity’ categories; these in turn affect diet, mental health, access, and
quality of health care directly responsible for the health outcomes recorded
for ‘non-White’ groups. Thus to solve ‘racial health disparities’ we must
1 <http://www.nih.gov/news/health/sep2010/nimhd-27.htm>. Accessed Oct. 2013.
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tackle deeper structural inequalities in the social, economic, cultural, and
educational opportunities among minority groups; we must eliminate the
social causes for this phenomenon. At stake for these experts are matters of
social inequality.
Experts in the life sciences and specifically in population genetics and
genetic epidemiology think differently. They think that ‘race’ and ethnicity
classifications can be biologically significant, independent of their history
of racist use. Human population genetics examines the genetic similarities
within groups of people (‘populations’) who have historically lived together
but in isolation from other groups. Population geneticists argue that some
inherited and heritable genetic features in people of different US ‘race/ethnic-
ity’ groups can distinguish them on average from people of a different group.
Further, there is some research on genetic markers that could help us figure
out who will respond better to a drug and there is an argument that such
markers vary regularly with ‘race’ and ethnicity. Some biomedical scientists
thus think that ‘race/ethnicity’ categories can pick out, consistently, some
differences of a biological sort and that these kinds of differences could be
causing some health disparities.
If we follow this research programme, investigating factors on a genetic
level that associate with how people of different ‘race/ethnicity’ respond to
medications, then another possible way to tackle ‘racial health disparities’
emerges:look for better designed drugs that are ‘personalized’ to deal with
the particular profiles of people depending on their ‘race’ or ethnicity. What
is at stake for professionals working in the biosciences then is in part a bio-
logical, medically interesting, genetic phenomenon that census categories of
‘race/ethnicity’ help pick out.
All the while, disparities in the average health of different ‘race/ethnicity’
groups are politically important, irrespective of their medical significance.
Economic, political, and educational resources are still unevenly avail-
able across these population groups in the US and also uneven across the
private pharmaceutical industry and national health providers. There is a
long history of racist science that abused people of colour and made claims
about genetic differences between ‘races’. It is thus challenging, especially
for experts from social science backgrounds, to see bioscientists’ projects as
acceptable. Genetics experts think their research could provide equitable and
effective solutions for drug development but social scientists see the mention
of genetics as an evasion of the real source of health disparities:historical
and continuing social, political, educational, and structural inequality across
‘race/ethnicity’ groups in the US. Thus the struggle for resources to under-
stand and give solutions to ‘racial health disparities’ ends up being a struggle
across disciplines, mirroring the complex struggles in the day-to-day lives of
people identifying with different ‘race’ and ethnicity.
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KEY:BE HUMBLE ABOUT WHAT YOUR DISCIPLINE
CAN SEE/SHOWIS AT STAKE
Often our disciplinary training is targeted at recognizing and handling spe-
cific kinds of phenomena (e.g. biological phenomena, not societal ones).
Specialization can make it so an expert sees only particular issues at stake.
How should you deal with competing views on what is at stake? This is the
kind of challenge that can weigh down a discussion or a research project
from the outset, before there is even a chance to work on findings. While
there is no formula for a universal solution there are some things to identify,
acknowledge, and discuss explicitly as a group.
Negotiating conflicting ideas is not a challenge particular to interdiscipli-
nary science; it also occurs in mono-disciplinary science and it can lead to
new ideas. Take for example physics and the idea of ‘length’. Classical physics
thinks of length as fixed for all observers:measuring the length of a shoe in
your room and the length of this shoe on a train does not change the meas-
urement. But special relativity understands length (and time) as changing
relative to the velocity of an observer and so the length of a shoe moving very
fast relative to an observer would be shrunken relative to a stationary one.
Correlatively time will move slower the faster you accelerate. So, concepts of
length and time in physics vary depending on whether we do Newtonian or
relativistic mechanics, i.e. moving at average velocities versus ones near the
speed of light. The fact that we have developed more than one idea about
length is a testimony to the progress of physics, even if relativistic ideas cre-
ated tensions and competitions in science. And even if classical ideas can be
defined as special cases of relativistic ones, we still use the old ideas to solve
simpler problems in physics.
At issue in interdisciplinary domains is lacking a common basis for decid-
ing who is right about a question:methods and expertise that can help sort
through competing explanations will be different across domains. Social sci-
entists look for different things for a good social science theory and geneti-
cists have different criteria for calling their work good. At the same time, the
very fact of having come together to solve a problem suggests there should be
some shared starting point for inquiry.
In STS we think of those common starting points as boundary objects:ideas
or tools that straddle disciplinary work boundaries (Star and Griesemer 1989).
These objects collect people and interests around them, and they help unify
as well as distribute work on a topic. At the same time, seemingly common,
available ideas often need to get founded within different disciplinary domains
to do proper scientific work. Different disciplines have different tools for pro-
ceeding to think precisely, and so can only use boundary objects meaning-
fully if they can articulate and found them within their particular scientific
context.
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In interdisciplinary work we are often in a bind between seeming to be
talking about the same boundary thing but in effect having different founded
ideas of it. In such cases it is worth backtracking in our thinking to see when
and how disciplinary thinking diverged and when it should.
In the case of racial health disparities one seemingly shared organizing idea
is ‘race’. Social science health professionals and bioscience experts all seem to
be talking about ‘race’. But are they?
‘Race’ is commonly used as a category for sorting people according to
their:(a)physical features such as facial attributes, skin colour, hair tex-
ture, etc., (b)ancestry, examining one’s parents and one’s parents’ parents,
and (c)geographic origin, especially as relating to continental regions,
such as Asia, Africa, America, or Europe. While the category of ‘race’ is
controversial, it is used across disciplines to draw out questions on social
justice and health. Some scientists use ‘race’ to study how (a)physical fea-
tures, (b)ancestry, (c)geographic origin matter for socio-economic phe-
nomena, such as education, financial or social achievement, legal rights
and benefits, etc. On the other hand, bioscience experts found notions of
‘race’ into their own research context by thinking about how (a)physical
features, (b)ancestry, or (c)geographic origin relate to biomedical phe-
nomena, such as disease-associated phenotype or genotype, evolutionary
process, etc.
One way to keep track of multidisciplinary investigations on ‘race’ is to dis-
tinguish between how social science and life science disciplines respectively
come to understand ‘race’, as ‘sociorace’ and ‘biorace’. Renaming these ideas
shows that they are discipline-specific ones and need not exhaust a phenom-
enon as complex as ‘race’. It might turn out that racial health disparities have
more to do with ‘sociorace’ than ‘biorace’ or it could be that ‘biorace’ differ-
ences really are much more significant than ‘sociorace’ disparities in how
people respond to a drug. But in either case it is important not to minimize
the effort and complexity of the problem(s) at stake by calling each others’
categories empty.
This multiplicity of understandings is something to work from in inter-
disciplinary settings. We do not yet have familiar ways of thinking together
about possible interactions across levels of life from the genetic to the envi-
ronmental and societal. Most mainstream models of reasoning about disease
and what causes it distinguish between genes and environment or between
‘nature’ and ‘nurture’, and focus on one or the other.
Interdisciplinary science need not negate context-specific understand-
ings—this would get rid of a lot of the science we already know. The hope
is that interdisciplinary viewpoints can synthesize new ways to understand
complex challenges. Once grouped together in collaboration, scientists will
often develop further special languages through day-to-day exchanges, as
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well as negotiations of discipline-specific terminologies, coming to form
project-specific or other context-specific vocabularies. Developing new lan-
guages and new ideas in interdisciplinary settings demonstrates that simple
explanations of a complex phenomenon will often not suffice for effective
communication. We need extra work to get competing understandings of
what is at stake sorted and negotiated. Figuring out what is at stake is a step
towards that project.
2.2 Second Challenge:How do we Work Together?
A second area of challenges for interdisciplinarity is figuring out which meth-
ods and analyses already available for each discipline to bring to the table,
and when and how to use them. People with different ways of conducting
research have to sort out which among their array of approaches should be
applied to a task.
Work in different disciplines often happens in different geographical spaces,
following different timelines and patterns of work activity. Coordinating
work (or peripheral activities) and respecting each member’s disciplinary
habits may seem practically impossible within the same team. Further, diver-
gences in methodology often couple with discipline-specific social worlds
and negotiating work between disciplines often involves negotiating differ-
ent work cultures (Strauss 1978). Often this is precisely the site where conflict
among interdisciplinary participants is found.
Our case study here is an interdisciplinary group of scientists working
to advise policy-makers on UK policy related to Population Ageing and
Migration. The group we will call ‘PAM’ joined expertise from the same aca-
demic institution in three main fields:(a)Engineering and Computer Science,
(b)Demography and Gerontology, and (c)Operational Research.
Our study of PAM was undertaken in the first eight months of the pro-
ject by Efstathiou so we got to examine some initial stages in the collabora-
tion. The social worlds of all three, computer scientists, social scientists, and
management scientists, are different. We focus here on computer science and
social science relations, as PAM members from these two disciplines had no
prior history of collaboration. We examine how the groups go about learning
about the issues they found to be at stake. We also consider the issue of infra-
structure for temporal coordination among collaborators, and for that issue
rely on research by Mirmalek among scientists and engineers conducting
remote-robotic exploration of Mars, NASA’s Mars Exploration Rovers mission.
KNOWING TOGETHER
Consider how PAM members study population ageing and migration. As
mentioned, PAM includes social scientists from the fields of demography
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and gerontology. Demography and gerontology study population change by
measuring social characteristics, such as age, ‘race’, sex, marital status, educa-
tional achievement, and how these characteristics change—with gerontology
focusing on changes related to ageing. Social science work in PAM is more
akin to social statistics than sociology, emphasizing quantitative outcomes
and models.
How do PAM social scientists produce knowledge about population ageing?
What is their ‘epistemic practice’? First consider how data is sourced. Social
statistics relies substantially on actually or distally interrogating living (or
data about deceased) individuals. Pursuing data, social scientists often design
surveys. They carefully think out what questions to ask whom and in what
order to get information about phenomena they think are important—and
ones they hadn’t considered as well. This information may possibly relate
to historical data about a population, and it may be possible to analyse for
recommendations on how to respond to foreseeable population changes as
societies. Will we need more nursing homes? Should we give pensions to
people later? Etc.
Typical concerns for PAM social scientists include choosing what groups to
interrogate, what surveys to consult or conduct, what variables have been or
should be recorded and how to align and compare information from different
data sources. Researchers seem to be at first seeking an accurate and compre-
hensive description of the social world, without a clear plan for intervention
and modification of characteristics recorded. Social scientists want to get
the right description now because they think this will increase the chances
of effective social interventions in the future. Still, in the first instance it
is description as opposed to intervention that is the aim of their epistemic
practice.
The practice of computer scientists in the PAM group is interestingly dif-
ferent. These members come from the field of computer science called ‘com-
plexity science’ that is especially tasked with studying complex systems,
i.e. systems that cannot be studied by breaking them into simpler combi-
nations of simpler parts. How do these complexity scientists work? To get
knowledge the complexity scientists will often rely on intervention first, then
description. Complexity scientists build simulations or models of a situation
of interest, based on some background knowledge and educated guessing,
and then play around with variables to establish interdependencies between
components. Interventions are seen as of essence for establishing how ‘sensi-
tive’ the system is to different factors and so for making inferences regard-
ing the causal processes involved in system behaviour. Modifying particular,
assumed characteristics of imagined agents to see how a model behaves is
the bread and butter of building simulations. So it is common practice for
PAM complexity scientists to intervene in virtual situations as opposed to
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carefully describing actual ones. At the same time complexity scientists do
view their tools as helping derive descriptions:Simulations can help describe
mechanisms affecting the complex phenomenon in question. These two
kinds of knowledge-gathering practice map onto what philosophers of sci-
ence traditionally take to be distinct kinds of reasoning:inductive and deduc-
tive reasoning, building theory through respectively observation and testing
hypotheses.
Notably, and related to the challenge of doctrine, what is at stake for com-
plexity scientists need not be a social phenomenon. What complexity sci-
entists study is first complex, then human or social. So, an ‘agent’ whose
behaviour is modelled in a simulation can represent an individual human
agent but also a population or a non-human structure, as long as the behav-
iour of the simulated agent displays characteristics of complexity, such as a
non-linear evolution or the possibility of global system characteristics emerg-
ing (irreducibly) from interactions between individual parts.
BEING TOGETHER
Built and temporal structures are important for interdisciplinary work.
Interdisciplinary group members necessarily have ways of working, commu-
nicating, and using technologies respective of their disciplinary backgrounds;
thus explicit attention must be given to establishing shared meanings of
technologies, processes, and goals.
Time, or temporality, is an essential component of any organizational struc-
ture, for any work environment (Zerubavel 1981). Clock time is a common
technology used across disciplines and institutions that provides a shared lan-
guage that supports communication among people and machines that may
have an otherwise limited shared language. And yet it is so ubiquitous that it
is often overlooked and underdeveloped in consideration of supporting the
variety of temporal norms, habits, and practices that researchers employ.
Work is temporally differentiated even in a simple research project such
as PAM social science and complexity, but for a richer example consider the
work of engineers, scientists, and ethnographers on NASA’s Mars Exploration
Rover (MER) mission 2003. This was an exploration of the planet Mars carried
out by two solar-powered robots remotely operated by scientists and engi-
neers on Earth (Squyres 2005). Mission members conducted their work while
situated in Pasadena, California, and organized their work schedules accord-
ing to the time of day on Mars; i.e. people located in the Pacific Standard
Time zone in California were working according to the time of day on Mars
where a day is approximately 24 hours and 39.6 minutes long. To convert the
time difference people on Earth had to set their clocks forward by 40 minutes
every day (for ninety consecutive Mars days). In this collaborative science
work, which brought together astrogeologists, atmospheric scientists, and
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mechanical engineers, to name a few, the mundane act of using clock time to
coordinate work and communication (i.e. science analysis, meetings, breaks,
meals, etc.) was novel and extra-terrestrial.
MER’s interdisciplinary group came up with various technologies to help
them tell time on Mars throughout their days and nights on Earth. Devices
created to tell time on Mars included spreadsheets, a modified digital alarm
clock, even modifying a traditional watch so that it ran slower. Despite differ-
ent disciplinary relationships to temporality, mission members were inspired
by and shared in the goals of producing Martian science via remotely oper-
ating robots on Mars; so much so that they cooperatively carried out addi-
tional work to support the very infrastructure provided by the organization
to support them (Mirmalek 2008). This ‘infrastructural maintenance work’
appears in the forms of technology modifications and social processes neces-
sary to maintain and support work, which for one reason or another are not
included in the formal set of social arrangements and technologies provided
for work. With disciplinary backgrounds and outlooks intact, mission mem-
bers shared readiness to perform infrastructural maintenance work.
KEY:LIVE IN EACH OTHER’S WORLD
Working together relies on sharing some understanding and experience of
each others’ tools for producing knowledge. Sharing can be helped by com-
bining approaches on specific problems and by obtaining broader under-
standings of each discipline’s reasons for their ways of working.
In our study of the PAM group, social scientists seem to rely on the assump-
tion that data collected today is relevant tomorrow. This assumes that signifi-
cant social world features have a more or less stable structure. Complexity
theorists reversely assume that interactions and unanticipated behaviours are
definitive parts of complex systems; future behaviours can emerge from yet
be distinct from current ones and in cases it might just not be possible to offer
any prediction of a system’s behaviour, letalone predictions based on old
data. This kind of divergence can cause friction. However it is generally good
to shine a light on these issues because these are great candidate locations for
developing new understandings and tools.
For example, a complexity scientist observed during a full team meeting
that transition probabilities (probabilities of transitioning from one role or
state to another, e.g. from being ‘single’ to being ‘married’) may vary with
time. For example, how likely one is to get married may be on average dif-
ferent now than in five years. As a result, data collected today may be of lit-
tle use tomorrow. The complexity scientist’s challenge sparked a discussion
between a demographer and the complexity scientist on whether simulating
partners’ behaviour during family formation could help predict variations
in future transition probabilities. Could we model how behaviours might
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change? The question seemed interesting but was not followed up in that
instance. Another lead social scientist responded to the exchange by reining
in the discussion back to a practical outcome:answering the grant proposal
questions not bigger ones.
This kind of exchange is common in interdisciplinary settings. Some peo-
ple in a team may be better able to follow each other’s thinking and relate
to their perspectives, while others feel less easy with venturing to a question
or realm outside the well-defined. Depending on the positions and responsi-
bilities of team members, these tendencies influence team dynamics differ-
ently. But both tendencies are important in an interdisciplinary practice. The
tendency to translate and share is productive for building a shared practice,
but it can also lead to creations outside the mandated, expected, and more
troublingly, the provable, so the attraction to what is known is important to
respect and negotiate.
It can also be a matter of circumstance that allows joint ventures to move
forward. In the PAM case, some interdisciplinary sharing was expedited by a
change in roles. Asenior demographer and a junior complexity scientist were
invited to give a joint presentation; the junior complexity scientist got ill, so
the presentation was given solely by the senior demographer, which moti-
vated her to prepare by intensively reading up on complexity science, with
direction from the junior scientist. This is an example of the growth in new
understanding from crossing disciplinary boundaries and hierarchy. Indeed,
the senior demographer described this sojourn into complexity science as an
experience of sudden illumination, as a light bulb going off, once she realized
one could build up different scenarios and see what the spread of possibilities
could be, instead of relying on survey data.
This episode demonstrates the gains from interchanging roles and mutual
learning. Openness to flexibility in roles among collaborators may enhance
mutual understanding and appreciation of each other’s work. It can help
establish explicitly what some relevant questions are and to have a greater
sense of responsibility for each other’s work.
2.3 Third Challenge:Work with Divergent Ethos
So far we discussed the challenges of figuring out exactly what is at stake for
different disciplines and how to bring different practices together:what we
broadly dubbed challenges of doctrine and discipline. The third challenge you
face when doing interdisciplinary work is managing the different and at times
conflicting values placed in this work by different stakeholders. As discussed,
the ethos of a practice regards the kind of character it forms and promotes. In
an interdisciplinary project aimed to tackle a socially relevant issue, experts,
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policy-makers, and publics with different ethos come together. Interdisciplinary
work will need to figure out what is ‘valuable’, as well as ‘valid’ or ‘accurate’, in
conversation with how experts or non-experts consider these points.
As someone working in that milieu, you need to consider at least:(a)what
society values about your work, (b)what scientists not familiar with your
expertise value about your work, and (c)what you yourself value about your
work. Here are some examples of how things can get complicated when you
try to combine all these.
First of all, a proper scientific method or result may not be acceptable once
we consider the social context of its application. What you may value in
one narrow, scientific context is the same thing to dismiss as useless or even
harmful, once you think of the bigger picture.
For example, as discussed in Chapters3 and 16, it is generally accepted
that one of the best ways to know if a drug or treatment works is to do a
‘randomized controlled trial’ or RCT. In this scheme, a group of patients is
divided randomly in two groups. The drug under test is given to one group
and a placebo (a sugar pill) to the other and we then measure any differences
in the health of one group versus the other. But is this always the best way to
test a treatment?
In one case, doctors used an RCT to test if a new surgical procedure
worked:the procedure called ‘extra-corporeal membrane oxygenation’
(ECMO), a system for pumping oxygen into the lungs of newborn babies at
risk of suffocation. It seemed that newborns who got the procedure tended
to survive, while without it many died. Still, it was deemed proper science
to test the method using an RCT. Expectedly babies in the control group
died. Running the RCT instead of trusting historical evidence that ECMO
worked was valued by scientists who wanted to ensure that their procedure
really worked. But it was arguably not valued by the parents of babies in the
control arm who died. Though this was a case of ‘proper’ science, the test
was arguably unethical:it allowed a foreseeable loss of human life (Worrall
2002).
The study is one example where doctors had to negotiate their ethos as
medical researchers with the value (and responsibilities) that we place on
human life. Ensuring interdisciplinary science is ethical relies on respect-
ing human interests and values, alongside the values that different scien-
tific methods have. As we see from Chapter9, if you misrepresent or do
not consider all the relevant facts, then the ethos of your research is ques-
tionable—and mistakes exacerbated by the socially sensitive nature of this
research. On the other hand, if you use unacceptable ways to come up with
facts or technologies, or if these are possible to misuse in foreseeable ways
that have not been controlled, then the ethos of the research is again often
questioned.
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KEY:BE REFLEXIVE; MAKE EXPLICIT AND DISCUSS YOUR AIMS AND
MOTIVATIONS THROUGHOUT
It is important not to assume knowledge of each other’s values and moti-
vations, nor indeed of your own aims as these are under formation.
Interdisciplinary motivations structure knowledge production and can revise
individual and discipline-specific aims.
One way to address varying ethos among a group, as with conflicting
worldviews or practices, is to figure out ways to make the differences visible.
Acknowledging such challenges at the outset of collaborative work and allow-
ing for the differences to be factors adds value in interdisciplinary knowledge
production, and potentially prepares group members to better understand
conflicts later in the process.
What you contribute to a collaborative project will depend on many fac-
tors, including the aspirations, prior experiences, and assumptions you bring
to it. It is important to have ways for collaborating members to openly reflect
on and express their motivations for doing interdisciplinary research. This
can both aid awareness and help assess and modify the power of these work
commitments in real time. Acknowledgements of this kind from the outset
of collaborative work can be tricky, as people may have to admit they care
about different things. However making differences explicit in an environ-
ment of curiosity and tolerance can help share and shape the collective ethos,
allowing for motivations and values to form explicit factors that matter in
knowledge production. Sharing a vision for the importance and ethos of an
interdisciplinary collaboration is key for doing the maintenance work needed
to support the infrastructures of work that are by default under formation.
3. Conclusion
To translate science results into solutions for big challenges we need to fig-
ure out how to best amalgamate already available, but diverse knowledge.
We must account for societal organizations, cultural values, and political
processes, options for technology innovation, and their possible, unin-
tended consequences. Further, for democratic governing we need research-
ers, entrepreneurs, policy-makers, and people with stakes in the decisions
(‘stakeholders’) to agree on some approaches. And we need those productive
interactions between scientists, social and ethical ethics experts, and stake-
holders early on in the process of policy and technology development as
opposed to afterwards. Once we have started along a given direction it is hard
to return to earlier stages of development, for example, to address impacts of
an approach or technology (think of reactions to genetically modified food).
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Social science, history, and philosophy as well as natural science are impor-
tant here. Social sciences, history, and philosophy examine relationships
between societies, technologies, and values. Research in STS shows that doing
science research, even natural science research, is strongly influenced by the
social and political context where the science happens—not only because
societies decide to fund different projects differently (or not at all) but also
because problems become apparent and possible to work on because of
the very communities and social structures that scientific labour relies on.
Reversely, the products of scientific and technological work change our eve-
ryday lives, give us new tools to raise new questions and solve older ones.
They shape what we worry about and how we behave. This actual mutuality
and interdependence of the development of social groups, material tools,
and theoretical understandings is why we must think about the social, the
human, and the scientific together to understand and reorient ourselves
towards challenging situations.
In this chapter we have discussed three related challenges of being interdis-
ciplinary investigated through particular examples:
• Finding a common basis for understanding what is at stake (doctrine)
• Deciding and coordinating how to deal with issues at stake (discipline)
• Justifying why we should pursue shared research on these matters
(ethos)
And we offered some keys to help manage these challenges:
• Be open about what your discipline can see/show is at stake
• Live in each other’s worlds
• Be reflexive and discuss your aims and motivations throughout
To develop guides for interdisciplinary collaboration we need more of all
three:(1)work on central issues at stake, (2)creating and testing models for
cooperation between science and socio-humanist scholars, and (3)jointly
assessing guidelines for such collaborations. Doing and applying interdisci-
plinary science successfully is a case-by-case matter, but preparing for the
kinds of challenges that may arise with some key responses to them is a good
start for learning to do interdisciplinary science well.
References
Alvargonzález, D. (2011). ‘Multidisciplinarity, Interdisciplinarity,Transdisciplinarity,
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Further Readings
Bowker, G., and Star, S. (1999). Sorting Things out:Classification and its Consequences.
Cambridge, MA:MIT Press.
Jasanoff, S., Markle, G., Petersen, J., and Pinch, T., eds (1995). Handbook of Science and
Technology Studies. NewYork:Sage.
Efstathiou, S. (2012). ‘How Ordinary Race Concepts Get to be Usable in Biomedical
Science:An Account of Founded Race Concepts’, Philosophy of Science,
79:701–13.
Mirmalek, Z. (2008). ‘Working Time on Mars’, KronoScope, 8(2):159–78.
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