This article was downloaded by: [The University of British Columbia]On: 26 March 2014, At: 10:52Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Social Epistemology: A Journal ofKnowledge, Culture and PolicyPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tsep20

The Roles of User/Producer Hybrids inthe Production of Translational ScienceConor M.W. Douglas, Bryn Lander, Cory Fairley & Janet Atkinson-GrosjeanPublished online: 26 Mar 2014.

To cite this article: Conor M.W. Douglas, Bryn Lander, Cory Fairley & Janet Atkinson-Grosjean(2014): The Roles of User/Producer Hybrids in the Production of Translational Science, SocialEpistemology: A Journal of Knowledge, Culture and Policy, DOI: 10.1080/02691728.2013.848951

To link to this article: http://dx.doi.org/10.1080/02691728.2013.848951

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

The Roles of User/Producer Hybrids inthe Production of TranslationalScienceConor M.W. Douglas, Bryn Lander, Cory Fairley, andJanet Atkinson-Grosjean

This paper explores the interface between users and producers of translational science(TS) through three case studies. It argues that effective TS requires a breakdownbetween user and producer roles: users become producers and producers become users.In making this claim, we challenge conventional understandings of TS as well as lin-ear models of innovation. Policy-makers and funders increasingly expect TS and itsassociated socioeconomic benefits to occur when funding scientific research. We arguethat a better understanding of the hybridity between users and producers in TS isessential to encouraging effective TS activities. In arguing for broader understandingsof the hybrid roles of user/producers in TS we rely on empirical observations madeduring our four-year (2006–2009) study of three translational pathways here labeledclinical, commercial, and civic. These pathways were identified in a large-scale net-work of scientists investigating the pathogenomics of innate immunity (i.e. “the PI.2network”). Through our examination of “user-firms” in the commercial TS case study,of patients and clinician-scientists as users in the clinical TS case study, and of bioin-formaticians as user/producers in the civic TS case study, we suggest that the iterativeand dialectical nature of TS blurs the lines between users and producers, renderingsuch distinctions arbitrary and sometimes misleading. We suggest that such a blurredboundary may be a constitutive, if underappreciated, component of TS.Acknowledging the important role of user/producers may be a crucial step in

Conor Douglas is a Post-Doctoral Research Fellow in Collaborations for Outcomes Research and Evaluation

in the Faculty of Pharmaceutical Sciences at the University of British Columbia, Canada.

Bryn Lander is a CIHR Banting and Best Canada Graduate Scholar at the Centre for Health Services and

Policy Research at University of British Columbia, Canada.

At the time of writing all authors were members of the W. Maurice Young Center for Applied Ethics at the

University of British Columbia, Canada.

Correspondence to: Conor Douglas, Faculty of Pharmaceutical Sciences, Collaboration for Outcomes Research

and Evaluation (CORE), University of British Columbia Vancouver Campus, Office number 4103A- 2405

Wesbrook Mall, Vancouver, BC Canada V6T 1Z3 Phone 604 822-8959. Email: conor.douglas@ubc.ca

� 2014 Taylor & Francis

Social Epistemology, 2014

http://dx.doi.org/10.1080/02691728.2013.848951

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

overcoming translational challenges currently facing the biomedical domain, and inappreciating broader transformations in science.

Keywords: Translational Science; Users; User-Producer; User Innovation; Transla-

tional Medicine; Boundaries

Introduction and Background

In recent decades we have witnessed widespread international convergence aroundpolicies that promote translation of public investments in scientific research into

tangible socioeconomic benefits (Atkinson-Grosjean 2006; Rip 2009; Roberts 2009;Slaughter and Rhoades 2004). In academic circles this has led to a growth in

science policy literature on cooperative research centers (Boardman and Gray2010), and in Canada these translational imperatives manifest in programs such as

the Networks of Centres of Excellence, Canada Foundation for Innovation, andGenome Canada and its provincial Genome Centres. In the face of these efforts

challenges abound, and translational goals are proving to be more easily set thanachieved (Martin et al. 2008; Wainwright et al. 2006; Wehling 2008). To sustainconfidence of the scientific community and funders, translational promises and

expectations relating to medical breakthroughs are being managed and rearticulat-ed by translational spokespersons (Douglas 2005), and the pharmaceutical and

medical devices industries are having to explain innovation failure to both share-holders and patients (Hedgecoe and Martin 2003; Hopkins et al. 2007). Problems

in realizing the socioeconomic benefits of research have also been identified in thepages of Social Epistemology in which it was suggested that in order to

increase the likelihood of societal benefit from academic research, a combination ofpolicies should be considered, including a system for reporting broader impacts andcollaborating with users to help identify the potential utility of research proposals.(Roberts 2009, 217)

This critical role of users within scientific and innovation processes has beenrecognized for some time, with early studies of scientific instrumentation (i.e. gas

chromatography, nuclear magnetic resonance, ultraviolet spectrophotometry, andtransmission electron microscope) showing that it is almost always the user—not

the instrument manufacturer—who recognizes the need, solves the problem via aninvention, builds a prototype, and proves the prototype’s value in use (von Hippel

1975). Furthermore, von Hippel has shown in this case it is the user whoencourages and enables the diffusion of his invention by publishing informationon its utility and instructions sufficient for its replication by other users—and by

instrument manufacturers (1975, 19).We argue here that a more nuanced understanding of translational pathways,

and the role of users within these pathways, facilitates the translation between

2 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

utility and discovery. To explore this claim, the first part of this paper sketchesout our model of translational science (TS), which is iterative and dialectical in

nature and includes multiple pathways. Also, in this section, we provide importantbackground on the role of users in innovation generally, and their place in models

of translational science, technology transfer, and cooperative research centers.Our methods section then describes the large-scale public-private genomics

research network through which we identified three nested TS case studies forfurther analysis. Within this large research network, these three nested case studies

are described. The first nested case study that is seen as an example of commercialTS describes a university spin-off pharmaceutical company. The second nestedcase study describes as an example of clinical TS, and outlines the work a

clinician-scientists working within an academic hospital’s research institute. Thethird nested case study discusses an example of civic TS, and analyses a

multi-centered university-based bioinformatics development team.The core of the paper extends Roberts’ call to “collaborate with users” (2009,

217) by investigating the role users play in the translational processes within ourown three case studies. Through our three case studies, we show that users take

diverse forms that are not always analogous to the consumers or publics describedin science and technology policy talk. In our view, this fuller understanding of

who users are and what they actually do in the production of TS is key for a moreeffective realization of TS and its various socioeconomic benefits. In doing so, wealso hope that our work contributes to scholarship in areas of social epistemology

that seek to understand the production and transmission of knowledge.In the final part of the paper, we posit that due to its iterative and dialectical nat-

ure, TS blurs the lines between users and producers, rendering such distinctionsarbitrary and misleading. We believe that this hybridity between users/producers is

an essential facet of successful TS, and that our work represents an important step inbridging studies in TS and user innovation studies. This pivotal role played by users/

producers in key translational moments leads us to advocate for a greater apprecia-tion of user/producer diversity, and for their more direct involvement in translationsbetween utility and discovery. Our belief is that this position could not only work to

answer this journal’s call to guide ‘contemporary knowledge enterprises, but also tore-orientate translational policies particularly in the area of health.

Understanding TS and the Role of Users in Innovation

The notion of moving research into practice has long received attention from pol-icy-makers. In the immediate postwar years, for example, Vannevar Bush argued

that socially useful applications of science could be steered, in a linear fashion, byincreasing state investments in basic research (Bush 1945). Since then policy-mak-

ers—as well as academics in management sciences and science policy—have soughtall kinds of ways to facilitate the flow of knowledge and technology between uni-

versities and industry. In his comprehensive review of the field of technology

Social Epistemology 3

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

transfer, Bozeman (2000) suggests a number of determinants that might influencethe effectiveness of this particular approach to steering science. Such determinants

include characteristics of the transfer medium (e.g. licences, copyright, etc.), thetransfer agent, the type of transfer object (e.g. scientific knowledge, technological

device, etc.), the technology recipient, and the demand environment (e.g. price fortechnology, substitutability, relation to technologies now used, subsidy, market

shelters) (2000, 637). In overview, the field of technology transfer, Bozeman seesthe transfer agent as the ‘institution or organization seeking to transfer the tech-

nology (e.g. government agency, university, private firm, characteristics of the set-ting, its culture, organization, personnel),’ and the transfer recipient is theorganization or institution receiving the transfer object (e.g. a firm, agency,

organization, consumer, informal group, institution and associated characteristics’(Bozeman 2000, 637). The author stresses that technology transfer effectiveness

should not be measured solely in financial terms, and suggests other meaningsincluding ‘political impacts, impacts on personnel involved, and impacts on

resources available for other purposes and other scientific and technical objectives’(Bozeman 2000, 628).

It is clear that work in the field of technology transfer is concerned with trans-lational dynamics, and is well suited for dealing with some of the kinds of com-

mercial translation that we describe below (i.e. between universities andcompanies). However, we find it less well equipped to handle dynamics related tohealth care service provision, or open-access research infrastructures (i.e. the cases

of clinical and civic translation that are included in our analysis). While, we fullyagree that transfer effectiveness should be measured in many different ways—as we

do ourselves in our thinking on different kinds of civic and clinical translationalscience discussed below—much of the technology transfer literature that Bozeman

reviews assumes that ‘the transfer agent’ and ‘the technology recipient’ are separateentities. What our cases studies describe below, and what some management sci-

ence literature is now coming to recognize (Bogers et al. 2010), is how these canbe one-in-the-same in the process of innovation and translation.

One such institutional apparatus that could embody both transfer and recipient

functions are the cooperative research centers (CRCs) mentioned in our introduc-tion. These are organizational entities that seek to ‘promoting technological innova-

tion, commercialization and, ultimately, social and economic outcomes’ (Boardmanand Corley 2010, 445-6). In their introduction to a Special Issue on CRCs in The

Journal of Technology Transfer, Boardman and Corley outline key social, scientific,and economic drivers behind the rise of these intuitions, which include: the

collectivization of research, the emergence of the cooperative paradigm for researchpolicy, and extra-organizational partnering and open innovation in industry (2010,

447–9). In their discussion of the role of the open innovation paradigm on the riseof CRCs, the authors suggest that ‘the use of external knowledge from varioussources including other established firms, start-ups, entrepreneurs, government labs,

and universities (both locally and abroad) has moved from a supplemental to aprimary driver for innovation’ (Boardman and Corley 2010, 448).

4 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

While beneficial in their own contexts, much of the literature dealing variouskinds of translational activities and institutions are only partial. For instance, weaccept that the context of open innovation has been key in establishing transla-

tional institutions like CRCs, but lament the fact that users have not been includedin Boardman and Corely’s long list of primary drivers for innovation. Since the

chain-linked model for innovation was first proposed by Kline and Rosenberg(1986), researchers in innovation studies and technology dynamics have been

studying the recursive nature of innovation and the role of users in the innovationprocess (Von Hippel 1988; Gelijns and Rosenberg 1994; Swan et al. 2007). We

build on these views and take a more expansive position that perceives TS as a setof non-linear, iterative, and recursive processes where actors, information, artifacts,

and knowledge move back and forth between contexts of discovery—or what hastraditionally been referred to as ‘basic science’–and contexts of application in pur-suit of socioeconomic utility (see Figure 1 above, and Atkinson-Grosjean and

Douglas 2010, Lander and Atkinson-Grosjean 2011).Importantly, our iterative view is one that focuses on the processes of transla-

tion rather than its outcomes. Furthermore, our translational model is in-part aresponse to the more recent focus in health policy that have pushed for more effi-

cacious forms of ‘knowledge translation’ through which knowledge is linked andexchanged between users and producers to expedite advances in medicine (Landry

et al. 2006; WHO 2006). Entire sub-fields have arisen focusing on ‘translation inmedicine’ (T), which has been characterized as a multiphase—and somewhatlinear—process that includes the movement of biomedical research into diagnosis

or treatment (T1), subsequent development into evidence-based protocols(T2) (Kerner 2006, 73), deployment into clinical practice (T3) (Westfall, Mold and

Fagnan 2007), and verification and evaluation for ‘real world’ impacts on health(T4) (Khoury et al. 2007).

However, rather than seeing translation as a homogenous process that refers toeverything at once yet nothing at all, our research on an international scientific

network—reported in detail elsewhere (Atkinson-Grosjean and Douglas 2010)—has identified that multiple heterogeneous translational pathways can co-exist

within the same research network. In the following, we focus on three cases oftranslation related to commercial, clinical, and civic activities; yet taken as casestudies of translation, this list is in no way meant to be exhaustive. We are not

claiming that there are only three translational pathways, but rather we have found

Figure 1 Model of iterative TS.

Social Epistemology 5

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

three examples of translation that we are calling “commercial,” “clinical,” and“civic.” The purpose of this paper is not to set hard and fast definitional categories

of translation, but rather to describe the role of users in translational processes.Based on the case studies described below, we believe that a common feature of

these heterogeneous pathways is a breakdown between user and producer roles insuccessful translational science. While the blurred boundaries between producers and

users in the knowledge production process is well recognized and richly described inscholarship as diverse as innovation studies (von Hippel 2005), science and technol-

ogy studies (Oudshoorn and Pinch 2003), and e-commerce and computer program-ming (Klein and Totz 2004); it has yet to be applied in the area of translationalscience and medicine. By bringing user studies and translational science together, we

believe that we are simultaneously making contributions to both areas of research.Interestingly, while recent research has indeed presented evidence that a stron-

ger reliance on users would be beneficial for the innovation processes, much of itmaintains a division between users and producers; referring to the need for “user-

driven research” (De More et al. 2010), customer-active innovation (von Hippel1978), or “user-producer interactions” (Laursen 2011).1 Unlike many of these pre-

vious studies, we suggest that the boundary between users and producers is in factblurred, and believe that such a blurred boundary may be a constitutive—if underap-

preciated—component of TS.To be clear, our conceptualization of users is not a responsive one that sees pro-

ducers or commercial companies involving clients or consumers in a needs-based

market research component of the innovation process, which is arguable exploit-ative. Instead, our work seeks to display how users themselves take action in the

actual processes of techno-scientific innovation and facilitate the translation oftheir own research into various domains of application and use.

Methods & Description of Case Studies

Three nested case studies illustrating different translational pathways were identifiedthrough extensive study of a network of academic scientists and their collaborators

investigating the pathogenomics of innate immunity (“the PI.2 network”) from2006 through 2009. Members of the PI.2 network were investigating the underlying

genetic functioning of the innate immune system in humans and animals.2 Oursocial science team was integrated into the PI.2 network as collaborators tasked with

examining the nature of TS, and scientists’ attitudes towards the translational pro-cess. To begin our task, we first conducted documentary analysis and pilot inter-views to identify the types of translational activities scientists engaged in, which was

followed by a larger survey of these translational activities within the entire network(see Atkinson-Grosjean and Douglas 2010 for more details). As a result of these

scoping exercises, we identified three nested case studies illustrating different trans-lational pathways within the network. Other translational activities that followed

different pathways existed within the PI.2 network, and are surely to be observedelsewhere. While these three cases do not form an exhaustive list of translational

6 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

pathways, we selected these three case studies (detailed below) because of theirrespective success in the translational process, their heterogeneity, and because of

their connections with the PI.2 network. Case studies were investigated throughqualitative semi-structured interviews (n = 80), ethnographic participant observa-

tion, and the analysis of related scientific literature.Qualitative software (ATLAS.ti) was used to explore and extract thematic data

from our surveys, interviews, and field notes. A grounded theory approach(Charmaz 2006) guided our approach. A common coding matrix was developed

for all three case studies that captured information on over 30 aspects such asfunding, knowledge production, research culture, research values, training and thetranslational interface. This code list evolved during the analysis process as we

reflected on emerging themes. To improve the reliability in applying the codingmatrix between team members, several interviews were coded by multiple mem-

bers. Variations in coding application were discussed and consistent definitionsagreed upon. A lead researcher for each case study then coded all remaining

transcripts. Discussions between lead researchers of the three case studies resultedin the recognition that the theme of blurred boundaries between users and

producers crossed the three case studies, and was an important element of transla-tional success in each case. Case studies were labeled commercial, clinical, and civic

to describe the dominant translational pathway observed in each case.

Commercial case study

This case study examined the academic immunology laboratory that was the centre

of the PI.2 network, as well as the small biotech firm Inimex Pharmaceuticals Inc,that spun-off from the lab’s original basic discovery of a set of peptides relating toinnate immunological response (Hancock 1999, 2001). The social science research,

on which this case study is based, took the form of qualitative interviews in theacademic lab beginning in April 2007 and continuing through to August of 2008

with participant observation in Inimex. A total of 35 semi-structured qualitativeinterviews were conducted across the two sites, and included the scientists who

founded Inimex, the executives and scientists at Inimex at that time, the PI.2 pro-ject leaders, senior researchers, and graduate student researchers from the academic

lab, as well as representatives from the university industry liaison office. Interviewquestions related to the scientific history of the work that lead to the creation of

Inimex, as well as the factors that helped or hindered that process of translatingbasic scientific work into a commercial company. Through the course of the par-ticipant observation, a member of our research team was provided an office and

set of tasks relating to the operations of Inimex with much meaningful observa-tions coming via formal and informal interactions.

Clinical case study

The clinical case study focused on two laboratories in the node that provided thePI.2 network with a clinical interface. These laboratories are located in a research

Social Epistemology 7

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

institute on the campus of an urban referral hospital. The clinician-scientists head-ing the laboratories spend approximately 80% of their time on research and the

remaining 20% as immunological specialists in the adjacent hospital. The purposeof the case was to identify the translational practices mediating the clinical and

research goals of these particular clinician-scientists and their laboratories. Ourdata collection for the case study spanned the period February 2007 through Feb-

ruary 2008, with follow-up interviews in spring 2009. We started with structuredsurvey interviews with the clinician-scientists and their laboratory teams (n = 16),

and with the collaborating university scientists (n = 4). Later, we conducted in-depth semi-structured interviews with the same sources. Survey and interviewquestions explored issues such as the translational activities pursued within the lab;

the role of funding agencies in supporting the work; training experiences and roleswithin the lab; and collaborations with other research groups. Between May and

August 2007, we placed a participant-observer in the larger of the two hospital-based laboratories to monitor day-to-day operations and followed-up with spo-

radic observations to the end of the study period.

Civic case study

In this civic case study, our empirical research focused on the role of the bioinfor-

maticians within the PI.2 network in developing and maintaining a bioinformaticsdatabase and suite of analytical tools called InnateDB and Cerebral. This bioinfor-

matics system was being developed with multiple purposes and multiple-targetaudiences in mind. Not only was it constructed as an internal tool for the PI.2

project team, but it was also done with an eye on facilitating systems-level biologymore generally through its inclusion of interactions not limited to innate immu-nity. As a result, InnateDB simultaneously targeted the PI.2 network, those within

wider research community of innate immunity, as well as academic non-peers in(systems) biology more generally.3 It was meant as a platform technology to build

knowledge, facilitate future discoveries, and assist in the early development offuture medical prophylactics and/or therapeutics. Our social science goal was to

observe the constraining and enabling factors in the construction of this bioinfor-matics system across the university collaboration, and to examine how the bioin-

formatics system served the various target audiences. From December 2007 to July2007, we conducted ethnographic participant research and qualitative semi-struc-

tured interviews with one part of the bioinformatics collaboration, and in Novem-ber and December of 2008, we conducted a series of follow-up interviews acrossthe two institutions (total n = 25). Our interviews included the heads of the bioin-

formatics lab, the leaders of the PI.2 network, the bioinformaticians designing thefront-end and logic of the system, the computer scientist writing the programming

code, and the curators who were manually inputting and managing the datasubmitted to the system.

8 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

Users in TS: Three Case Studies

Role of “User-Firms” in the Commercial Case Study

This case study was characterized by translation between the laboratory and, ulti-

mately, the market. In our case study of this instance of commercial translation,we tracked the discovery and exploitation of a group of novel synthetic com-

pounds called innate defense-regulator (IDR) peptides, which were found to bepotential anti-infective agents (Scott et al. 2007). We followed the IDR peptidesfrom the academic lab, through the patenting and licensing process, and into a

biotechnology spin-off company established to develop their “drug-ability” (i.e.making the peptides commercially viable as a potential human therapeutic). Ini-

mex was established in 2001 following the filling of patents related to the peptides,and the granting of intellectual property rights from the university from which the

discovery was made.Firms and other commercial entities participate in translation as users in vari-

ous ways, including the licensing of technologies developed within the academy,public–private partnerships, and encouraging research areas through scholarshipsand grants. In the case of the PI.2 network, the granting agency—Genome Canada

—mandated the involvement of private sector partners in successful large-scaleproposals. This requirement usually entails a company providing funding in

exchange for licensing rights or exclusive access to discoveries, and a company Ini-mex4 filled this role for PI.2.

Originally based on the university campus, Inimex eventually moved off-site.Over time, the academic research scientists involved in the company were replaced

with staff of the new biotechnology start-up and the company became increasinglypursuant of commercial goals and products. Of importance to the topic at hand is

that some of these staff members were part of the university lab, but then transi-tioned to Inimex staff. This movement embodied the hybridity of the producer/user. However, due to the fact that the company and the research project were ini-

tially led by the same key actors, many of the early motives and goals were alsosimilar. Within the project, Inimex was both an initial partner aiding in project

design, as well as the projected user of the human health-related output of theproject.5 The early involvement of Inimex, as a user, impacted the P.I2 project by

influencing its research trajectory and setting end goals of marketable therapeutics.One of the members of staff told us about the role the company was playing as a

user-firm in the translational process:

You just have to be careful that you still have room for the people who are doingbasic research. And maybe the room for them is providing them a way to translatetheir research out, without them having to do it. Maybe that is the motivation. Ifwe’re product centred, then it’s all about getting the products out. Then let’s providethem a way to get the basic research into a product, without requiring that they,themselves, to do it. Which is clearly probably neither their desire nor their skill set.

Social Epistemology 9

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

Research in the PI.2 project was based in part around the development of IDRpeptides, and understanding their ability to selectively modulate the immune

response. Despite holding promise as infection-fighting therapeutics, early ver-sions of the peptides consisted of long and complex strings of proteins making

them difficult and expensive to produce; and thereby lacked “drug-ability.” Withthe needs of the user-firm in mind, research efforts were realigned to synthesize a

smaller, easier-to-produce, and more cost-effective peptide. The subsequent reduc-tion in complexity and cost to synthesize the peptide benefited both the company

and the academic research. The point to note, however, is that the specific needsof the user—in this case the commercial firm—were incorporated into theresearch project early on, largely because they had an active role as users in the

academic research project. As a member of the company described this processby stating:

… a better thing to start looking at is, if you’re going to make this, if you’re going tomanufacture this, is it going to be easy to make? Is it going to be really expensive?Because if it’s going to be really expensive, then forget it. So, all these other propertiesthat aren’t as important to maybe the mechanisms, but they’re important to its drug-ability—if you want to use that—then you start to look at some of these other candi-dates.

The above description suggests at least two interpretations. First, with regard tosmall start-up biotechnology companies, there seems to be deep user/producer

integration early in the project and company life cycle. However, this level of inte-gration tends to recede as the technology moves further down the developmentpipeline and the interests, goals, and methods of the academic and commercial

labs diverge; at which point the conventional user and producer roles arereestablished. Second, because of the close early relationship between the academic

scientists, their labs, and the company, the needs of the user-firm—as opposed to“imagined users” who might later take up the technology as a commercial product

—were accommodated within project design and implementation. The integrationof users thus guides translational practices and increases the likelihood of

translational success.

Role of Patients and Clinician-Scientists as Users in the Clinical Case Study

The clinical case study was characterized by the movement of information relating

to medical problems and solutions back and forth between the research laboratoryand the clinic.6 Focused on novel diagnostic techniques and protocols, it involved

direct translation between the “bedside” and the “bench.” Our case study of theclinical pathway explores the identification of a rare immunological disorderknown as IRAK4 deficiency in one patient, and the subsequent creation of a diag-

nostic protocol (von Bernuth et al. 2008). IRAK4 deficiency is an extremely raredisease with only 32 cases documented worldwide by 2008 (von Bernuth et al.

2008). In a movement often characterized as “bedside to bench” (Martin, Brown,

10 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

and Kraft 2008), a pediatric patient was referred to a research hospital to investi-gate the possibility of an immunological disorder in 2001. A clinician-scientist

working both as an immunological specialist within the hospital and as a principalinvestigator with his own scientific research lab saw the patient and conducted

standard immunological tests that produced no diagnostic results. Based on thepatient’s case history, the clinician-scientist was convinced that the patient had an

immunological disorder and decided to study the case in his own lab. The childwas prescribed prophylactic penicillin to prevent further infections.

Over the next two years, researchers within the lab analyzed samples and datagathered from the patient in an attempt to elucidate the basis of his immunologi-cal disorder. The lab was ultimately able to diagnose this patient with IRAK4

deficiency in 2003 based on a publication that identified the new disorder of IRK4deficiency (Picard et al. 2003). With the disease identified, work then moved back

from “the bench” to “the bedside” with the patient’s diagnosis being used toinform treatment, and develop a diagnostic tool for IRAK4 deficiency. Work also

moved further into the realm of “the bench” as the clinician-scientist’s lab subse-quently collaborated with more “basic” scientists on genotyping and sequencing

IRAK4 deficiency using the patient as a model.Patients and clinicians have traditionally been identified as the users of clinical

TS through the creation of new clinical processes and practices (Gelijns, Zivin, andNelson 2001; Windrum and Garcia-Goni 2008). Patients are users of clinical TS inso far as services are performed for their benefit, and their preferences influence

the successful adoption of a clinical innovation. Clinicians are often the operativeusers of diagnostic tools or devices resultant from clinical TS, and also dictate

patient access to specific medical treatments.The IRAK4 deficiency case study illustrates the highly iterative nature of clinical

TS, and the often blurred role between users and producers within the process. Indue course, this IRAK4 deficient patient was a user of his diagnosis, but was also a

key initiator of the clinical translation process by playing a central role in theresearch and the development for the novel clinical diagnostic protocol as the pro-vider of both the clinical problem and the necessary related patient data. As one

PhD student in the lab explains:

the patient’s family was really motivated and they gave permission to draw someblood in order to look at things that aren’t covered by standard clinical laboratorypanels.

Described by our source as “experiments of nature,” such patient-based research

questions appear to be the foundation of much of clinical TS, which can thereforebe seen as highly user-centered. Perhaps, more interesting for our discussions here,

the clinician-scientists also became active users and producers of clinical TS in atleast two ways. One was their central role in the construction of the diagnostic tool

for IRAK 4 deficiency that they would use in clinical practice, and the other wastheir work—in collaboration with other scientists—to transfer the clinical problem

Social Epistemology 11

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

and data to guide the labs’ research agenda. As one of the clinician-scientistsnoted:

I start from the patient, and start from what I see as important clinical questionswhere I can offer some insight … and then I try as much as possible to use samplesfrom the patient, blood, DNA, other information.

Results of this research were then fed back to clinical contexts and used by clini-cian-scientists in their role as clinicians to inform the care of the young immuno-

deficient patient.This case demonstrates the importance of a blurred boundary between users

and producers in clinical settings and TS. In doing so, it aligns itself with a novelpolicy direction that provides a more significant role for patients in research(Boote, Telford, and Cooper 2002; Oliver et al. 2004) and provision of care

settings (Crawford et al. 2002; Department of Health 2007); and for individualsworking, as the clinician-scientists did in this case, in an active hybrid role as users

and producers of clinical TS.

Role of Bioinformaticians as User-Producers in the Civic Case Study

The civic case study involved iterative processes within PI.2 bioinformatics team to

foster freely available research tools for the use and benefit of all. Thebioinformatics system in question is called InnateDB. It is a platform and suite of

tools developed as a part of the PI.2 project that equips project team members andoutside users (i.e. their academic non-peers) alike with open source and open

access tools to approach the complexities of innate immunology from a systems-level perspective. The bioinformaticians who developed InnateDB to address their

own key questions of innate immunity have played a central role in translatingwhat could have been an in-house and project-specific tool into a highly accessibleand publicly available resource. InnateDB provides “integrated bioinformatics and

visualization tools for the systems-level analysis of the innate immune response”(InnateDB 2009). It goes beyond being an exclusive tool for innate immunologists

by including “detailed annotation on the entire human, mouse, and bovine inter-actomes by integrating data (195,000 + interactions & 3,900 + pathways) from the

several of the major public interaction and pathway databases”.7 As such, it standsto facilitate the work of others across a diversity of scientific communities (Aderem

and Shmulevich 2008), hence making it a good example of civic TS focused ontranslation within the scientific polis or academic non-peers.

Some of the key users involved in this translation process were thebioinformaticians who developed InnateDB.8 These self-identified bioinformati-cians were not only the prototypical users on which the DB was largely configured

for PI.2 purposes, but also the developers of parts of the system itself. Accordingto one of InnateDB’s developers:

12 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

We recognized what we wanted to do as biologists doing data analysis and we builtthe system with that as our end goal really, keeping in mind that you know whatmost people will end up doing with the system, partially hypothesis discovery.

A key component of InnateDB was the production of its visualization toolCerebral (or Cell Region-Based Rendering And Layout) (Barsky et al. 2007) that

“will enable biologists without a computational background to explore their datain a more systems-oriented manner” (Lynn et al. 2008, 2). Developed by bioinfor-

maticians within the PI.2 project principally as a tool to answer their own ques-tions about innate immunity, Cerebral was also produced with an eye to how it

could be used to benefit investigators outside of the project team and even beyondthe field of immunology. As one of the developers of Cerebral explained,

you know [Cerebral is] a component of Innate Db, but in and of itself it’s, its ownproject. And so I really, you know when I was doing the Cerebral work; I tried todevelop it for the larger community.

Which larger community, sorry?

Biology in general, anybody interested in visualizing networks in a pathway like fash-ion. So you know, it’s all basically, its creation was inspired by Innate Db, and sort ofwent along with Innate Db, but always I always kept my eye towards a larger audi-ence when developing it.

By creating an effective tool to address their own needs, these bioinformaticiansalso took significant steps to equip themselves—as well as other researchers—with

the apparatus needed to make sense of the massive amounts of genomic data thatcan stifle translational advances. As a consequence, they were at once producers

and users of InnateDB. Further, Cerebral—a Cytoscape plug-in9—has capitalizedon developments within the emergent fields of information visualization and visual

analytics to help investigators who may lack computational expertise to “detect theexpected and discover the unexpected” (Thomas and Cook 2005) through “a

combination of layout efficiency and interaction techniques [that] provides anintuitive, biological context-based method of visualizing and interacting with net-works of up to several thousand nodes” (Barsky et al. 2007, 1041). If the masses of

genomic data generated from current R&D activity are to be developed into healthand economic benefits, then forms of TS activities like this will be needed. Com-

munity-orientated tools like InnateDB, which is configured to users’ needs by usersthemselves, will only stand to facilitate translational processes.

Discussion and Conclusion—Understanding Users in TS

While TS may be neither an entirely novel phenomena (Littman et al. 2007), noran uncontested term (Woolf 2008), work presented here makes a number of clear

contributions to both existing TS frameworks and to user studies—primarily bybringing these disparate areas of study together. Here, our position is that users—in

their diverse and heterogeneous forms—shape the direction and development of TS

Social Epistemology 13

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

as they simultaneously take on the role of producers of the science and technologythat they will ultimately use. Through our commercial, clinical, and civic case stud-

ies of TS, we have demonstrated that different kinds of TS display different levelsand forms of user/producer hybridity in TS processes. That said, all three TS cases

involved a breakdown of user and producer roles. While common across our threecases, the degree of user/producer hybridity appears to vary according to transla-

tional pathway and the particular instance of TS examined. Nevertheless, theseusers/producers take hybrid forms that see them concurrently involved in produc-

tion and use activities as they span across pathways of scientific development.In the commercial case study, Inimex was involved with production of immu-

nological research by inscribing the realities of marketable therapeutics early on in

the R&D stage through a deployment of their expertise as a “user-firm.” This isparticularly interesting in light of other research (Zomer, Jongbloed, and Enders

2010) that has described “exchange relationships” between Dutch public researchorganizations (PROs) and research based spin-off companies (RBSO) without hav-

ing “a significant impact on the direction of the research conducted at the PROs”(2010, 348). The Dutch case studies found that RBSOs “contribute, mostly in a

modest way, to research activities by providing information, equipment and mone-tary resources. More importantly, RBSOs were seen as helpful for researchers com-

peting for research grants that demand participation of industry. RBSOs enhancethe prestige of their parent organizations and create legitimacy for public fundsinvested in PROs” (2010, 348). While these contributions can also be seen in the

case of commercial TS described here, we have shown how Inimex was alsoinvolved in early research production stages by stressing the need for a smaller,

easier-to-produce, and more cost-effective peptide that it would later use for com-mercial development.

In the clinical case, patients and clinician-scientists also played key productiveroles in the initiation and development of research surrounding the IRAK4

deficiency by pursing a research program that they would use in clinical practice.In some ways, this clinical case study can be seen as an example of a larger trendin health towards more patient-centered medicine (Lane and Davidoff 1996). It is

also important to note the role played by clinician-scientists as active user/pro-ducer within this process. In the IRAK4 project, clinician-scientists embedded

within larger formal and informal networks of practitioners and researchers under-took crucial “articulation work” (Fujimura 1987), working sometimes as users and

sometimes as producers, and in this hybrid role translating between clinicians andscientists.

Finally, the civic case study described how bioinformaticians consciouslydesigned and produced InnateDB and Cerebral to facilitate their own systems-level

work on innate immunity, and to simultaneously provide a set of tools that wouldbe accessible and useful to others outside their project team and discipline (i.e.their academic non-peers). Utility of the system was secured—in part—by taking

the needs of the larger community of biologists into account in the productionprocess. The visualization tool Cerebral can be seen as an example of

14 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

“convergence,” which sees “the merging of distinct technologies, processing disci-plines, or devices into a unified whole that creates a host of new pathways and

opportunities” (MIT 2011, 4). While convergence is important and represents apowerful discourse in twenty-first century biomedical R&D, we believe that for

convergence between disciplines and technologies to be successful there must alsobe convergence between users and producers. Bioinformatics is littered with expe-

riences of algorithms and interfaces that are incomprehensible and unusable tobiologists (Bolchini, Finkestein, and Paolini 2009; Javahery, Seffah, and Radha-

krishnan 2004; Pavelin et al. 2012). To be sure, computational techniques can bedeployed to make sense out of biological systems, but unless those bioinformaticssystems are designed with and for biologists—similar to what Letondal (2006)

refers to as “participatory programming”—they will never be used.In all three cases described here, the lines blurred between users and producers

to the extent that those who are most likely to make use of the translationalproduct are also involved at crucial developmental stages. Not only does this dem-

onstrate their significance in TS practices, but it also holds importance for the PI.2network. By being able to identify a number of different forms of successful TS,

the PI.2 team will be able to easily demonstrate its translational accountability toresearch funders, which will also stand them in good stead when future research

applications are being developed. More importantly, by describing the role thathybrid user/producers play in TS, we hope that the PI.2 team—and other research-ers like them—will continuously and conscientiously integrate these kinds of actors

into their work thereby facilitating translational processes.On top of these contributions to our object of study, work presented here also

offers contributions to the TS frameworks that currently dominate the discourse.Our research is amongst the first to explore multiple cases and forms of transla-

tional together in a single paper. In doing so, our work underscores the fact thattranslation can take multiple and varied paths.10 This diversity needs to be

recognized by policy-makers and funders who want to encourage and accordinglyevaluate all forms of translation; rather than be unduly focused on clinical or com-mercial instantiations of TS. In making this claim, we have been careful to ground

our insights in empirical case studies, moving discussions of TS beyond the modelsand visions often employed by research directors or policy-makers (see e.g. Collins

2011 or Zerhouni 2005). We have stressed that the empirical cases explored hereare not the only possible TS pathways. Our contribution is not directed at creating

definitions of translational categories, but rather at describing the role of usersacross translational pathways. Our research revealed translational examples that we

are calling commercial, clinical, and civic TS, yet they could easily be calledsomething else, or be used to describe a wider range of activities.11

Importantly, our understanding of successful TS sees a blurred boundarybetween development and deployment, and, as a consequence, between users andproducers. Our perspective stands in contrast to existing models of TS that operate

on (linear) models of research, development, and deployment that maintain firmboundaries between producers and users (Khoury et al. 2007). Within

Social Epistemology 15

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

conventional TS frameworks, knowledge flows only one way with research (T1)that over time moves into evidence-based protocols (T2), clinical practice (T3),

and finally for its assessment of its health impact (Lander and Atkinson-Grosjean2011, 538). As a consequence, we suggest those interested in TS to explore user

studies as a fruitful area of scholarship. As a supplement—or alternative—to exist-ing TS frameworks, we offer here an empirically driven model that makes sense of

how translation has taken place across three different pathways.While funding bodies and science and technology policy-makers want

translational success from the projects they support, this is more easily mandatedthan delivered. Policy objectives for different translational pathways diverge. Incommercial translation, public policy directs resources to promote “knowledge-

based” economic growth and high-skills jobs in industries such as biotechnologyand pharmaceuticals, and to aid in the development of new therapeutics, devices,

and so on. Public policy and resources support clinical translation to generate thesocioeconomic returns associated with a healthier population. Civic translation can

be supported to further the scientific enterprise that ultimately facilitates othertranslational objectives, or to foster science and society interactions. Policy-makers

need to recognize that translation can follow these and other pathways. Supportingand evaluating translation, in its heterogeneity, are key for policy-makers commit-

ted to translational goals. A policy stance supporting user/producer hybriditythrough direct integration within TS projects, and in future studies into TS, canhelp further these heterogeneous policy goals and pathways and facilitate delivery

under these translational mandates of socio-economic benefit.This paper proposes an empirically derived model in which the boundaries

between users and producers are increasingly porous, to the extent that a user/pro-ducer hybrid is created, and actors who are likely to make use of a technology are

increasingly involved in the upstream production processes. While socialepistemology has long been interested in ‘how knowledge operates as a principle

of social organization’ (Fuller 2001: x), our work offers a complementary interestin hybrid actors who are altering the ways in which such knowledge is producedand used.

Acknowledgments

Funding for this research was provided by Genome Canada through the Pathoge-

nomics of Innate Immunity (PI.2) project. At the time the research was conducted,all authors were affiliated with the Centre for Applied Ethics at the University ofBritish Columbia, Canada. Thanks to the three reviewers who provided helpful

feedback, and to the Journal’s editor James Collier, which lead to important revi-sions of this work. All authors have no conflicts of interests relating to this work.

Notes

[1] The blurring of user/producer boundaries is not topic that has gone unnoticed as evi-denced by scholarship on the “prosumer” (i.e. producer-consumers) and “prosumption”

16 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

(i.e. production-consumption) in informationcommunication sciences (Zwick, Bonsu, andDarmodt 2009), media studies (van Dijck 2009), marketing (Kozinets, Hemetsberger, andJensen Schau 2008), or sociology (Comor 2010; Ritzer and Jurgenson 2010). However,there are debates with regards to the novelty of the prosumer phenomenon (Ritzer andJurgenson 2010; Toffler 1980), as well as its implications. Some authors argue that “collec-tive consumer innovation” that is manifested by prosumption represents a fundamentaltransformation to “… the nature of consumption and work and, with it, society …”(Kozinets, Hemetsberger, and Jensen Schau 2008, 339). Yet, other analysts view the phe-nomenon as in the context of hegemony in which the prosumer is “at the very least, thesubject of ongoing exploitation and, quite possibly, an agent of increasingly complexforms of possessive individualism (Comor 2010, 322).

[2] This system of innate immunity is the first line of immunological defense; it allowshumans “to withstand a daily onslaught of tens of thousands of potentially pathogenicmicrobes in air, food and water, and in our interactions with other people and animals”(PI.2 website http://www.pathogenomics.ca/ Accessed June 7th, 2012 and has increased ininterest with the rise of resistant ‘superbugs’ like MRSA.

[3] While a different term could be used to describe all of the different forms of translationinvolving academic peer communities, academic non-peer communities (i.e. the scientificpolis), the lay public, etc. for the ease of writing, and in keeping with our conceptualiza-tion and naming of TS pathways elsewhere (see Atkinson-Grosjean and Douglas 2010), wewill use the term “civic TS” here while noting that we are referring to translation relatingto the scientific polis or academic non-peer communities.

[4] Inimex Pharmaceuticals is a company started by two UBC research scientists to specifi-cally commercially develop a discovery made in their academic labs.

[5] Another corporation—Pyxis—held a similar role with relation to animal relateddevelopments.

[6] This case study has been described in greater detail elsewhere and will only briefly be out-lined here (see Lander and Atkinson-Grosjean 2011).

[7] Given the ongoing curation of this database and bioinformatics systems, the number ofinteractions and pathways is constantly being updated. These numbers were taken fromthe InnateDB website on 1 May 2012 accessed at http://www.innatedb.org/.

[8] We accept that the term “bioinformatician” can be a somewhat contested one as the areaof research and development is so new, and as its practitioners are numerous and hetero-geneous. While an analysis of what characterizes “bioinformaticians” is surely of interestfrom a social studies of science standpoint, it is work that is being carried out by ourgroup elsewhere and not the subject of treatment in this work. For our purposes, herebioinformaticians are those who self-identify as such.

[9] Cytoscape is an open source bioinformatics software platform for visualizing molecularinteraction networks and biological pathways and integrating these networks with annota-tions, gene expression profiles and other state data. (http://www.cytoscape.org/ accessedJanuary 26th, 2009).

[10] Not only do other translational pathways exist, but much work has already been describ-ing them without using the language of TS or research. For instance, the participation ofscientists in policy discussions with government; public engagement exercises on contro-versial technologies such as bio-banking (Haddow et al. 2008), stem cell research (Theise2003), or genetically modified foods (Horlick-Jones et al. 2007); and citizen-to-citizen for-ums such as Cafe Scientifique and Minimed Schools can all be seen as examples of trans-lation that have not been discussed in this paper. See both www.cafescientifique.org/index.htm and http://science.education.nih.gov/mms/Description for more information onCafe Scientifque and MiniMed Schools.

Social Epistemology 17

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

[11] For instance, the term “civic TS” suggests a very wide range of activities that describetranslation involving a number of different communities: the academic peer community,the academic non-peer community (scientific polis), and the lay public. In fact, separatecategories of TS could be created for each translational pathway, and as we have notedabove our case-study work does not represent and exhaustive list.

References

Aderem, A., and I. Shmulevich. 2008. Taming data. Cell Host & Microbe 4 (4): 312–13.Atkinson-Grosjean, J. 2006. Public science, private interests. Culture and commerce in Canada’s

networks of centres of excellence. Toronto: University of Toronto Press.Atkinson-Grosjean, J., and C. Douglas. 2010. The ‘Third Mission’ and the laboratory: How

translational science engages and serves the community. In Community engagement andservice mission of universities, edited by Hans Schuetze and Patricia Inman, pp. 309–23.Leicester: NIACE.

Barsky, A., J. L. Gardy, R. E. W. Hancock, and T. Munzner. 2007. Cerebral: A cytoscape pluginfor layout of and interaction with biological networks using subcellular localization anno-tation. Bioinformatics 23 (8): 1040–42.

Boardman, C., and D. Gray. 2010. The new science and engineering management: Cooperativeresearch centers as government policies, industry strategies, and organizations. Journal ofTechnology Transfer 35: 445–459.

Bogers, M., A. Afuah, and B. Bastianl. 2010. Users as innovators: A review, critique, and futureresearch directions. Journal of Management 36: 857–875.

Bolchini, D., A. Finkestein, and P. Paolini. 2009. Designing usable bio-information architectures.Human-Computer Interaction 5613: 653–62.

Boote, J., R. Telford, and C. Cooper. 2002. Consumer involvement in health research: A reviewand research agenda. Health Policy 61: 213–36.

Bozeman, B. 2000. Technology transfer and public policy: A review of research and theory.Research Policy 29: 627–655.

Bush, V. 1945. The endless frontier. Transactions of the Kansas Academy of Science 48 (3): 231–64.

Charmaz, K. 2006. Constructing grounded theory: A practical guide through qualitative analysis.London: Sage.

Collins, F. S. 2011. Reengineering translational science: The time is right. Science TranslationalMedicine 3 (90): 1–6.

Comor, E. 2010. Contextualizing and critiquing the fantastic prosumer: Power, alienation andhegemony. Critical Sociology 37 (3): 309–27.

Crawford, M. J., D. Rutter, C. Manley, T. Weaver, K. Bhui, N. Fulop, and P. Tyrer. 2002. Sys-tematic review of involvement of patients in the planning and development of health care.BMJ 325: 1263–68.

De More, K., K. Berte, L. De Marez, W. Joseph, T. Deryckere, and Luc Martens. 2010.User-driven innovation? Challenges of user involvement in future technology analysis.Science and Public Policy 37 (1): 51–61.

Department of Health, Chief Medical Officer. 2007. The expert patients programme’. Availablefrom <http://www.dh.gov.uk/en/Aboutus/MinistersandDepartmentLeaders/ChiefMedical-Officer/ProgressOnPolicy/ProgressBrowsableDocument/DH_5380844> (accessed 15 January2009).

van Dijck, J. 2009. Users like you? Media, Culture and Society 31 (1): 41–58.Douglas, C. M. W. 2005. Managing HuGE expecations: Rhetorical strategies in human genome

epidemiology (HuGE). Science Studies 18 (2): 26–45.

18 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

Fujimura, J. H. 1987. Constructing ‘do-able’ problems in cancer research: Articulating alignment.Social Studies of Science 17 (2): 257–93.

Fuller, S. 2001. Social epistemology. 2nd ed. Bloomington, IN: Indiana University Press.Gelijns, A. C., J. G. Zivin, and R. R. Nelson. 2001. Uncertainty and technological change in med-

icine. Journal of Health Politics, Policy and Law 25 (5): 913–24.Haddow, G., S. Cunningham-Burley, A. Bruce, and S. Parry. 2008. Generation Scotland:

Consulting publics and specialists at an early stage in a genetic database’s development.Critical Public Health 18 (2): 139–49.

Hancock, R. E. W. 1999. Host defence (cationic) peptides: What is their future clinical potential?Drugs 57 (4): 469–73.

——— 2001. Cationic peptides: Effectors in innate immunity and novel antimicrobials. LancetInfectious Diseases 1: 156–64.

Hedgecoe, A., and P. Martin. 2003. “The drugs don’t work”: Expectations and the shaping ofpharmacogenetics. Social Studies of Science 33 (3): 327–64.

Hopkins, M. H., P. A. Martin, P. A. Nightingale, A. Kraft, and S. Mahdi. 2007. The myth of thebiotech revolution: An assessment of technological, clinical and organisational change.Research Policy 36 (4): 566–89.

Horlick-Jones, T., J. Walls, G. Rowe, N. Pidgeon, W. Poortinga, G. Murdock, andT. O’Riordan. 2007. The GM debate: Risk, politics and public engagement Oxon. Abingdon:Routledge.

InnateDB website. 2009. Home page. Available from www.innatedb.ca/index.jsp (accessed 1August 2009). INTERNET.

Javahery, H., A. Seffah, and T. Radhakrishnan. 2004. Beyond power: Making bioinformatics toolsuser-centered. Communications of the Association for Computing Machinery 47: 59–62.

Kerner, J. F. 2006. Knowledge translation versus knowledge integration: A “funder’s” perspective.Journal of Continuing Education in the Health Professions 26 (1): 72–80.

Khoury, M. J., M. Gwinn, P. W. Yoon, N. Dowling, C. A. Moore, and L. Bradley. 2007. Thecontinuum of translation research in genomic medicine: How can we accelerate theappropriate integration of human genome discoveries into health care and disease preven-tion? Genetics in Medicine 9 (10): 665–74.

Klein, S., and C. Totz. 2004. Prosumers as service configurators—vision, status and futurerequirements. In E-life after the Dot Com Bust, edited by Brigitte Preissl, Harry Bouwman,and Charles Steinfield, pp. 119–34. Heidelberg: Physica-Verlag.

Kline, S. J., and N. Rosenberg. 1986. An overview of innovation. In The positive sum strategy,edited by R. Landau, and N. Rosenberg, pp. 275–305. Washington, DC: National AcademyPress.

Kozinets, R., A. Hemetsberger, and H. Jensen Schau. 2008. The wisdom of consumer crowds.Journal of Macromarketing 28 (4): 339–54.

Lander, B., and J. Atkinson-Grosjean. 2011. Translational science and the hidden research systemin universities and academic hospitals: A case study. Social Science and Medicine 72: 537–44.

Landry, R., N. Amara, A. Pablos-Mendes, R. Shademani, and I. Gold. 2006. The knowledge-valuechain: A conceptual framework for knowledge-translation in health. Bulletin of the WorldHealth Organization 84 (8): 597–602.

Lane, C., and F. Davidoff. 1996. Patient-centered medicine: A professional evolution. JAMA 275: 152–6.Laursen, K. 2011. User–producer interaction as a driver of innovation: Costs and advantages in

an open innovation model. Science and Public Policy 38 (9): 713–23.Letondal, C. 2006. Participatory programming: Developing programmable bioinformatics tools

for end-users. In End user development, edited by Henry Lieberman, Fabio Paterno andVolker Wulf, pp. 207–42. Dordrecht: Springer.

Littman, B. H., L. Di Mario, M. Plebani, and F. M. Marincola. 2007. What’s next in translationalmedicine? Clinical Science 112: 217–27.

Social Epistemology 19

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

Lynn, D., G. L. Winsor, C. Chan, N. Richard, M. R. Laird, A. Barsky, J. L. Gardy, F. M. Roche,T. H. W. Chan, N. Shah, R. Lo, M. Naseer, J. Que, M. Yau, M. Acab, D. Tulpan, M. D.Whiteside, A. Chikatamarla, B. Mah, T. Munzner, K. Hokamp, R. E. W. Hancock, andF. S. L. Brinkman. 2008. InnateDB: Facilitating systems-level analyses of the mammalianinnate immune response. Molecular Systems Biology 4 (218): 1–11.

Martin, P., N. Brown, and A. Kraft. 2008. From bedside to bench? Communities of promise,translational research and the making of blood stem cells. Science as Culture 17 (1):29–41.

MIT. 2011. White paper the third revolution: The convergence of the life sciences, physical sciencesand engineering. Massachusetts Institute of Technology, Washington, DC Office.

De More, K., K. Berte, L. De Marez, W. Joseph, T. Deryckere, and Luc Martens. 2010. User-dri-ven innovation? Challenges of user involvement in future technology analysis. Science andPublic Policy 37 (1): 51–61.

Oliver, S., L. Clarke-Jones, R. Rees, P. Milne, J. Buchanan, J. Gabbay, G. Gyte, A. Oakley, andK. Stein. 2004. Involving consumers in research and development agenda setting for theNHS: Developing an evidence-based approach. Health Technology Assessment 8 (15): 1–154.

Oudshoorn, N., T. Pinch, eds. and Comps. 2003. How users matter: The co-construction of usersand technologies. Cambridge, MA: MIT Press.

Pavelin, K., J. A. Cham, P. de Matos, C. Brooksbank, G. Cameron, and C. Steinbeck. 2012.Bioinformatics meets user-centred design: A perspective. PLoS Computational Biology8 (7): e1002554.

PI.2 project website. 2006. About the project. Available from http://www.pathogenomics.ca/(accessed 17 December 2009). INTERNET.

Picard, C., A. Puel, M. Bonnet, C. L. Ku, J. Bustamante, K. Yang, C. Soudais, et al. 2003.Pyogenic bacterial infection in humans with IRAK 4 deficiency. Science 299: 2079–2079.

Rip, A. 2009. Putting the K back in: Knowledge, research, and innovation systems (KRIS). InSTEPS Seminar, 9 February, University of Sussex, England. Available from http://www.slideshare.net/Stepscentre/manifesto-seminar-arie-rip (accessed 28 July 2009).INTERNET.

Ritzer, G., and N. Jurgenson. 2010. Production, consumption, presumption—The nature of capi-talism in the age of the digital ‘prosumer’. Journal of Consumer Culture 10 (1): 13–36.

Roberts, M. R. 2009. Realizing societal benefit from academic research: Analysis of the nationalscience foundation’s broader impacts criterion. Social Epistemology 23 (3–4): 199–219.

Scott, M. G., E. Dullaghan, N. Mookherjee, N. Glavas, M. Waldbrook, A. Thompson, A. Wang,et al. 2007. An anti-infective peptide that selectively modulates the innate immuneresponse. Nature Biotechnology 25: 465–72.

Slaughter, S., and G. Rhoades. 2004. Academic capitalism and the new economy: Markets, state,and higher education. Baltimore, MD: Johns Hopkins University Press.

Swan, J., A. Goussevskaia, S. Newell, M. Robertson, M. Bresnen, and A. Obembe. 2007. Modesof organizing biomedical innovation in the UK and US and the role of integrative andrelational capabilities. Research Policy 36 (4): 529–47.

Theise, N. D. 2003. Stem cell research: Elephants in the room. Mayo Clinical Proceedings 78:1004–9.

Thomas, J., and K. Cook. 2005. Illuminating the path: The research and development agenda forvisual analytics. Los Alamitos, CA: IEEE Computer Society Press.

Toffler, A. 1980. The third wave. New York: Morrow.von Bernuth, H., C. Picard, Z. Jin, R. Pankla, H. Xiao, C. -L. Ku, M. Chrabieh, et al. 2008.

Pyogenic bacterial infections in humans with MyD88 deficiency. Science 321 (5889):691–96.

von Hippel, E. 1975. The dominant role of users in the scientific instrument innovation process.Research Policy 5: 212–39.

20 C. Douglas

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014

——— 1978. A customer-active paradigm for industrial product idea generation. Research Policy7: 240–66.

——— 1988. The sources of innovation. New York: Oxford University Press.——— 2005. Democratizing innovation. Cambridge, MA: MIT Press.Wainwright, S. P., C. Williams, M. Michael, B. Farsides, and A. Cribb. 2006. From bench to

bedside? Biomedical scientists’ expectations of stem cell science as a future therapy fordiabetes. Social Science & Medicine 63: 2052–64.

Wehling, M. 2008. Translational medicine: Science or wishful thinking? Journal of TranslationalMedicine 6 (31): 1–3.

Westfall, J. M., J. Mold, and L. Fagnan. 2007. Practice-based research—“blue highways” on theNIH roadmap. JAMA 297: 403–6.

WHO. 2006. Bridging the “Know-Do” gap: Report on the knowledge-translation in global health.Geneva: WHO.

Windrum, P., and M. Garcia-Goni. 2008. A Neo-Schumpeterian model of health servicesinnovation. Research Policy 37: 649–72.

Woolf, S. H. 2008. The meaning of translational research and why it matters. JAMA 299 (2):211–13.

Zerhouni, E. A. 2005. Translational and clinical science—Time for a new vision. New EnglandJournal of Medicine 353 (15): 1621–3.

Zomer, A., B. Jongbloed, and J. Enders. 2010. Do spin-offs make the academics’ heads spin?Minerva 48 (3): 331–53.

Zwick, D., S. Bonsu, and A. Darmodt. 2009. Putting consumers to work. Journal of ConsumerCulture 8 (2): 163–96.

Social Epistemology 21

Dow

nloa

ded

by [

The

Uni

vers

ity o

f B

ritis

h C

olum

bia]

at 1

0:52

26

Mar

ch 2

014