{"id":3036,"date":"2014-09-09T21:57:13","date_gmt":"2014-09-09T21:57:13","guid":{"rendered":"http:\/\/peerproduction.net\/?page_id=3036"},"modified":"2016-03-10T08:16:48","modified_gmt":"2016-03-10T08:16:48","slug":"shared-machine-shops-as-real-life-laboratories","status":"publish","type":"page","link":"http:\/\/peerproduction.net\/editsuite\/issues\/issue-5-shared-machine-shops\/peer-reviewed-articles\/shared-machine-shops-as-real-life-laboratories\/","title":{"rendered":"Shared Machine Shops as Real-life Laboratories"},"content":{"rendered":"
Sascha Dickel, Jan-Peter Ferdinand and Ulrich Petschow<\/strong><\/p>\n From its very beginnings modernity could be described as a social formation which values innovation. It embraces the production of new ideas, practices and technologies. The task of innovation, however, was usually carried out by specialized experts (inventors, researchers, and developers) in specialized areas (laboratories of universities, research centers, and R&D departments).<\/p>\n As long as only a small sector of society engages in innovation it might be an exaggeration to speak of modernity as an innovation society, but in the light of recent developments the diagnosis of an innovation society is gaining new plausibility. Innovation has become heterogeneously distributed, ubiquitous, and reflexive: Innovation is increasingly produced by decentralized networks which involve actors from divergent social fields. Innovation therefore leaves the traditional sphere of the restricted laboratory and is transformed into an ubiquitous practice which is also adopted by non-professional as well as non-commercial actors like sports enthusiasts (Baldwin, Hienerth & von Hippel, 2006), private tinkerers (Baldwin & von Hippel, 2011), or \u201einnovation communities\u201d in general (von Hippel, 2006: 96). Hence, the growing knowledge about innovation also leads to a reflexivity of innovation itself (Hutter et al., 2011: 2), extends the scope of innovative practices, and transforms the very processes and structures of innovation: findings from the fields of open source software (Raymond, 2001; Kogut & Metiu, 2001), crowdsourcing (Brabham, 2008; Howe, 2010), or the modes of open-\/user-based innovation mentioned above show evidence for these broader transformations.<\/p>\n According to \u00d8stergaard et al. (2013) innovation nowadays is primarily network-driven, structured by new modes of communication, interaction, and production that have emerged from the internet. It is increasingly acknowledged that innovation is not something that happens inside organizational boundaries, but rather a complex social process that transgresses the borders of labs and R&D departments. Since Castells\u2019 description of the \u201cRise of the Network Society\u201d (Castells, 1996) the diagnosis of a transformation of social structures in reaction to the emergence of new media of communication has become commonplace in the social sciences (Baecker, 2007 & 2011). One important feature of our innovation society is its hyper-complexity. Society has reached a stage of complexity which makes it impossible to predict complex social developments (like market developments). Inter alia, this is because of an ongoing differentiation of individual values and preferences, the formation of new forms of interaction and \u201ccrowd\u201d behavior, and a growing technological infrastructure that supports the rapid diffusion of ideas in an unprecedented way.<\/p>\n These technological and cultural changes challenge the central role of the organization as the center of innovation. In the economic literature, Coase (1937) asked the question why firms exist in the first place (and not just markets). He reconstructed the function of the organization in the economic sphere as a medium to decrease transaction costs. Within the boundaries of the organization complexity is reduced, and a common pool of resources and knowledge is established. Innovation as a social process is more likely if several experts with specialized skills come together under the umbrella of an organization. Organizations allow for a coordination of innovation practices.<\/p>\n With the rise of the Internet a different medium of coordination has emerged. Not only has it become possible to interact with people across the globe, but also to share ideas which might contribute to the emergence of an innovation. To gain access to the expertise that is desired and needed for innovation practices (but located outside of the organization) is much easier nowadays (Lakhani et al., 2013). It has also become evident that people without formal and certified expertise (Collins, 2002) might contribute to innovations and that the \u201cSchumpeterian momentum\u201d noticeably shifts from the producer to the customer (Grabher, Ibert & Flohr, 2008: 255). All of these dynamics signify recent organizational reactions to the network-based paradigm that, from the perspective of a focal enterprise, is generally summarized as \u201copen innovation\u201d (Chesbrough, 2011).<\/p>\n The \u201crapid increase in the number of citizen science initiatives\u201d (Roy et al., 2012: 9) demonstrates that, even in science, a social sphere that is traditionally defined by a clear boundary between (organized) expert knowledge and lay knowledge, the power of \u201cboundary-spanning processes\u201d (Hoffmann, 2012) is increasingly recognized: On the platform zooniverse alone over a million volunteers participate in scientific projects via crowdsourcing. Other projects like foldit utilize the creativity of users through an online game that encourages user to discover new protein structures (Haklay, 2013). The International Genetically Engineered Machines Competition (iGEM) \u2013 an important event in the field of synthetic biology \u2013 is now even opening its doors for teams of do-it-yourself biologists (http:\/\/diybio.org\/2013\/11\/06\/diy-igem\/).<\/p>\n In parallel to (and partly in conjunction with) the opening of organizations and expert communities, new figurations of actors have appeared who share knowledge and information outside organizational boundaries. They invent and create, first and foremost, without being motivated by money. Open Source Software like Linux and novel modes of knowledge generation like Wikipedia are the prime examples of this new mode of decentralized and hierarchy free \u201cpeer production\u201d which is defined as \u201cdecentralized, collaborative, and nonproprietary; based on sharing resources and outputs among widely distributed, loosely connected individuals who cooperate with each other without relying on either market signals or managerial commands\u201d (Benkler 2006: 60; see also Al-Ani, 2013). Peer Production depends on self-selected and heterachical practices: People do contribute to platforms like Wikipedia because they want to (for whatever reasons), not because of a hierarchical directive.<\/p>\n Decentralized peer production is nothing completely new. By drawing on examples from the 19th century, Allen (1983) and Nuvolari (2004) coined \u201ccollective invention\u201d as a similar mode of commons-based collaboration between companies who share their knowledge and expertise in joint innovation processes. Also, \u201cScience is built by many people contributing incrementally \u2013 not operating on market signals, not being handed their research marching orders by a boss \u2013 independently deciding what to research, bringing their collaboration together, and creating science. What we see in the networked information economy is a dramatic increase in the importance and the centrality of information produced in this way\u201d (Benkler, 2006: 63; see also Gl\u00e4ser, 2006). What Benkler emphasizes with his notion highlights the wider inclusion of actors, which is facilitated by information technology that unleashes potentials of local, temporal, and cultural diversity.<\/p>\n In this paper we will contribute to the discussion on new forms of innovation and production by focusing on one further similarity of current forms of peer production and science: experimental practices. Following recent discussions on real-life experiments in science and technology studies, we will argue that experimentation is an important feature of innovation practices. Just like innovation, experimentation has also become a ubiquitous, heterogeneously distributed and reflexive practice. Especially in the recently emerged community- and peer-based forms of production, the freedom to experiment plays a major role. In contrast to the limitations of experiments embedded in hierarchies and the imperatives of formal organizations, peer communities provide settings where actors are primarily intrinsically motivated and free to join and leave these communities and this is likely to cause an increased freedom to experiment. We suggest that experimental practices are not something that happens in addition to other things going on in peer production contexts, but that peer production itself is a real-life-experiment in societal transformation.<\/p>\n The transformation of society by means of peer production is a guiding vision of some authors engaged with new forms of collaboration. They suggest that the very logic of capitalism might be transformed by this mode of coordination. However, so far the most notable success stories in terms of transformation (Wikipedia and Linux) are limited to the digital realm. One important precondition for the growth of peer production beyond the digital realm is the connection of decentralized collaboration in digital networks with material forms of production (Bauwens, 2005; Zuboff, 2010). With the current focus on the importance of networks and digital media it is easy to forget the relevance of physical spaces of innovation. But, especially when the result of a coordinated effort is not an immaterial good but something tangible (like a piece of hardware), physical infrastructures and material resources beyond digital platforms are necessary. The existence of science could be interpreted as a proof of concept that the links between decentralized information networks (publications) and sites of engagement with material objects (laboratories) could be established. We therefore introduce the concept of real-life laboratories as new places for experimental innovation practices in contexts of peer production.<\/p>\n Shared machines shops (SMS) are a perfect example of these new laboratory spaces. They embody the values of ubiquitous, heterogeneously distributed and reflexive experimentation. They provide new laboratory infrastructures outside of hierarchical organizations while being embedded in the digital and fluid networks of a new experimental culture. However, like social studies on laboratory life have shown, the boundaries between the laboratory and the rest of society are not absolute (Latour, 1983). We use two examples of innovations in shared machined shops (low-cost-prosthesis and open hardware 3D printers) to demonstrate that peer production as a new form of innovation is still in a fragile niche phase. It is surrounded by an innovation regime that implicates commercial logics and patterns of market regulation and thus reveals tensions with the particular practices of experimental exploration which are constitutive for the open and community-based approach of SMS.<\/p>\n Shared machine shops like FabLabs, TechShops, maker- and hackerspaces are relatively new phenomena. As this special issue demonstrates, these workshops are currently a rather \u201chot\u201d topic. They are framed as nuclei of collaborative grass-roots fabrication that could revolutionize and democratize manufacturing or may even replace capitalist patterns of production and consumption (Smith et al., 2013: 4). But are shared machine shops actually the constitutive elements of a new industrial revolution (Anderson, 2012), or will they remain idiosyncratic niches? We think that it is still too early to answer a question like this. Maybe the question itself is wrongly phrased. In this paper we will offer a different perspective on shared machine shops. These workshops can be taken as experimental settings where new visions, practices, and technologies are developed, tested, and refined. SMS are laboratories of a new kind. These laboratories are neither detached from society, nor are they only accessible for professionals. Instead, shared machine shops are real-life laboratories. In the following section we will elaborate on this concept.<\/p>\n Experiments are a defining feature of modern science (Hacking, 1983; Pickering, 1995; Rheinberger, 2002). In the scientific worldview, not the study of ancient traditions, but the inquiry of nature by means of experimentation is regarded as the most suitable path to knowledge. The paradigmatic place of experimentation is the laboratory, a special setting constructed for the performance of experiments. In the traditional, truth-seeking form of knowledge production, labeled \u201cmode 1\u201d by Gibbons et al. (1994), the function of the experiment was the construction of facts \u2013 and the function of the laboratory was the \u201cpurification\u201d of this construction (Latour, 1993). Local observations in these controlled settings form the basis for the generalization of facts which can be transferred to the outside world. As long as the central goal of science was the pursuit of truth, the boundaries of the laboratory could hardly be solid enough in order to exclude outside influences which might contaminate the experiment and would hamper insights into cause and effect relationships. In innovation society this mode of knowledge production has been displaced by more context-driven and problem-focused projects. In this so-called \u201cmode 2\u201d (Gibbons et al., 1994) of knowledge production, experiments often take place outside controlled settings \u2013 or even beyond purely scientific contexts.<\/p>\n It might be wrong, however, to identify experiments with pure (or purified) science in the first place. In his analysis of the relation between experiments and modernity, Krohn (2007) has shown that the semantics of experimentation can be found in heterogeneous contexts of modern life such as experimental literature, wars (as contexts for the experimental use of new weapons) and experimental forms of urban development. In all these contexts the term \u201cexperiment\u201d is used to designate systematic learning practices by means of specific technical or social installations. Learning is not used as a normative term here, but as an analytical concept. Learning occurs if individuals or social systems break with established routines and create something new. Learning makes use of \u201cirritations\u201d that change the \u201cusual way of processing information\u201d (M\u00f6lders, 2014: 2). In experiments, social, technical and\/or natural conditions are ordered and arranged in a specific way to encourage this kind of learning from irritations, and hence the establishment of new routines.<\/p>\n It is this systematic approach to learning by means of remodeling (material or immaterial) conditions that distinguishes experiments from those practices of trial and error that occur in everyday life on a regular basis, and sometimes even unintentionally. Experiments allow it to try something new and risky, and to accept the occurrence of failure. Furthermore, experimental settings make it possible to learn from those mistakes in a systematic manner. Experiments, therefore, combine an amount of freedom and control not usually found outside experimental settings.<\/p>\n Krohn notes that Darwin\u2019s evolutionary theory of variation and selection might suggest that nature itself is experimenting (Krohn, 2007: 346). This, however, confuses the categories of evolution and learning. Evolution is blind; learning is the result of reflection (M\u00f6lders, 2011). Evolution produces variations bottom-up, without intention, planning, or control. Learning can also occur spontaneously, but \u2013 especially in modern society \u2013 we can observe the emergence of institutional orders that aim to encourage learning, like laboratories or classrooms. While not only natural change, but also social change is mostly the result of evolution, in modernity learning has become an important alternative mode for the production of variations, of new ways of doing things that break with established routines. Also experiments, those special techno-social arrangements for learning, now take place in many different social contexts (Krohn, 2007).<\/p>\n In innovation societies the need for experimental learning has widely increased. In cases like genetic field experiments, prototyping in research and development, or beta releases of software products, experiments become real-life experiments (Krohn, 2007; Gro\u00df et al., 2003): Real-life experiments take place outside scientific laboratories. They don\u2019t follow the logic of isolation and purification of laboratory experiments and typically include actors outside professional scientific contexts. Their objective is not the generalization of natural laws but the exploration of specific cases (Krohn, 2007: 349-354). Gro\u00df even suggests that nowadays controlled laboratory experiments have become the exception, while real-life experiments have become the norm (Gro\u00df, 2013: 196).<\/p>\n While experiments might have left the closed spaces of scientific institutions, the world outside these institutions is also changing. Krohn and Gro\u00df suggest that society itself turned into an experimental setting that itself begins to resemble laboratory life (Gro\u00df & Krohn, 2005).<\/p>\n Laboratories in contexts of research and development are institutional spaces that create a boundary between science and society. What happens in laboratories should not bother the rest of society \u2013 and vice versa the outside world should not be bothered by the small socio-technical world of laboratory life. The world inside the laboratory becomes \u201ca world on probation\u201d (Krohn, 2007: 348, translated by the authors). But even in the world of pure science this boundary between the inside and outside world is fragile, as science studies have shown (Latour, 1983). On the basis of the more general notion of experiment developed above, the concept of the laboratory can also be expanded. Laboratories are not only closed rooms detached from the rest of society, they can be all kinds of (more or less protected) spaces in which the arrangements necessary for experimentation can be installed. Hence, laboratories are not only places in which facts are produced and reproduced but also \u2013 and maybe foremost \u2013 places that facilitate installations and constellations which enable irritation and learning (which again may or may not form the basis of new facts). This more open understanding of laboratories can be traced back to the Chicago School of Sociology and is currently revitalized in science and technology studies as well as in environmental science (Latour, 1983; Gro\u00df & Krohn, 2005; Schneidewind & Scheck, 2013).<\/p>\n In environmental science the concept of real-life laboratories (Schneidewind & Scheck, 2013) was recently developed to describe semi-protected spaces that are established for experiments between knowledge generation and knowledge application; where new kinds of socio-technical practices are developed and tested. A real-life laboratory is neither a closed room, designed to control all relevant experimental boundary conditions, nor a borderless space like \u201csociety\u201d, \u201cthe market\u201d or the \u201cinternet\u201d. Real-life laboratories instead create a semi-open spatial and social microcosm, where failures are allowed, irritations are welcome, and learning is encouraged.<\/p>\n An important feature of real-life laboratories is their transdisciplinarity and openness. Not only certified experts can gain access to these places. They are rather spaces that encourage the interaction of experts and so-called \u201clay persons\u201d, who might indeed be (uncertified) \u201cexperts\u201d as well and who can contribute to ongoing real-life experiments. In the closed space of traditional laboratories in universities and R&D departments of firms, the presence of these non-certified experts would usually not be allowed (at most as \u201csubjects\u201d of an experiment or \u201cvisitors\u201d to the laboratory) and their knowledge would be excluded from the processes of innovation, experimentation and collaborative learning (Collins & Evans, 2002).<\/p>\n In their study of research \u201cin the wild\u201d, Callon and Rabeharisoa (2003) have shown that there is no intrinsic difference between expert knowledge and lay knowledge. \u201cIt would, for example, be wrong to say that the former are explicit and codified while the latter are tacit, or that the former are formalized while the latter are informal. Everything depends on the equipment used on both sides and, more broadly, the conditions \u201cin which the expertise is produced\u201d (ibid.: 196). Real-life laboratories can be conceived as laboratories \u201cin the wild\u201d in which the boundaries between expert and lay knowledge can get blurred even more, because real-life laboratories might provide the equipment and conditions for knowledge production typically associated with the world of scientific expertise.<\/p>\n Nevertheless, it is important to keep in mind that real-life laboratories are not designed for purification purposes. They are laboratories with a different function than traditional scientific laboratories: In the context of innovation society real life-laboratories provide niches for path-breaking innovations. The concept of niches was developed within an evolutionary multi-level perspective on innovation and transition (Geels, 2011; Geels & Schot, 2010). It highlights the importance of hegemonic socio-technical regimes as selection environments for innovative variations. They constitute stable and dominant ways of realizing societal functions. These regimes form the \u2018deep structure\u2019 of socio-technical systems. They can be understood as interrelated social rules and routines, cultural beliefs and practices and technical infrastructures that guide the activities of policy makers, market actors, and engineers alike (Geels, 2011: 27; Smith et al., 2010: 441).<\/p>\n Regimes encourage incremental innovations along certain paths, but create structural disadvantages for path-breaking innovations and therefore limit forms of learning. Niches provide spaces where new ideas and technologies are developed while being (partially) protected from the dynamics of the current socio-technical regime: Niches shield path-breaking innovations against the selection pressure of the regim, and they nurture these innovations through the (1) articulation of expectations and visions, (2) the building and expanding of social networks, and (3) the encouragement of learning on technical, economic, political, and cultural dimensions (Smith & Raven, 2012: 1026\u20131030; Geels, 2011: 28). The more the focus of niches is on learning, the more the function of niches as an experimental setting comes to the fore.<\/p>\n In the recent literature on real-life laboratories, those settings are typically understood as rather large entities like cities, regions, or organizations (Expertengruppe \u201eWissenschaft f\u00fcr Nachhaltigkeit\u201c, 2013: 16). In our paper, however, we apply the concept to places whose scope and design is closer to traditional laboratories: shared machine shops like hackerspaces, makerspaces, FabLabs and TechShops. Our understanding of SMS as real-life laboratories is based on a document analysis of the self-descriptions of different types of shared machine shops, interviews with participants and exploratory field observations.<\/p>\n If applied to large and more or less unbounded areas like cities, the concept of the real-life laboratory might remain a rather metaphorical description. In contrast to this metaphorical usage, shared machine shops are defined by specific locations. They are places with a distinct identity shaped by experimentation, innovation, and learning. However, unlike traditional laboratories, SMS are not integrated into an organizational hierarchy and they embody the blurring of boundaries between experts and lay persons (or \u2013 following Collins and Evans (2002) \u2013 certified and non-certified experts). In the case of SMS the latter could, for example, be students, hackers, makers, and hobby inventors.<\/p>\n Shared machine shops can be understood as real-world laboratories that develop and test not only new technologies but also new practices in the dimensions of creativity, sustainability, and inclusivity (Smith et al., 2013: 5\u20136). They are laboratories for (technical and social) innovations, where design ideas can be shared, a hands-on mentality can be cultivated, and new skills can be acquired. They might also be places of serendipity, where experts and professionals meet with hobby enthusiasts and DIY innovators and work together on new, unexpected projects. In some cases sustainability is an explicit goal of such spaces. Technologies of digital fabrication like 3D printing (which is constitutive for FabLabs and TechShops and commonplace in maker- and hackerspaces) are often framed as green technologies, because of the additive production process and the possibility to produce goods locally. Furthermore, shared machine shops might engage in recycling and upcycling of products, and subscribe to post-consumerist values. Shared machine shops can also foster a more inclusive form of innovation and production, and give marginalized parts of the population access to tools and networks; hence, they are also associated with hopes for user empowerment (Smith et al., 2013: 5\u20136; Dickel, 2013; Walter-Herrmann & B\u00fcching, 2013).<\/p>\n It is still unclear which of these promises can really be fulfilled by shared machine shops, but maybe this is not entirely the point. If we think of SMS as real-world laboratories it is not so important if the shops themselves are already decentralized factories of a new kind with perfectly sustainable and inclusive modes of production that must only be up-scaled. If we take a step back and view them foremost as real-world laboratories it is more important to systematically learn from the practices in these workshops. Learning from their successes can therefore be as important as learning from their errors. The multidisciplinary communities and networks which connect these workshops at local, national and transnational levels can then not only be framed as an emerging (maker) movement but also as a new \u201cexperimental culture\u201d (Rheinberger, 2002: 149\u2013150), a networked community which inspires the ways in which new laboratories are constructed, reflects on the experiments made, and may change and adapt them if necessary. The creation and alteration of shared workshops can then be understood as a second order experiment: a large scale experiment where every real-life laboratory is itself a unit of experimentation.<\/p>\n How the results of these experiments are used, however, may easily escape the sphere of influence of the laboratories. Will the ideas, technologies and practices developed in shared machine shops be integrated in the existing regime of production, or may they serve as blueprints for a new socio-economic regime? This leads to the question of empowerment, a third function of niches (besides shielding and nurturing) which was recently analyzed by Smith et al. (2012). The authors understand empowerment as practice that increases the competitiveness of innovations when they are brought into the world outside the niche. Empowerment can be realized in two ways: (1) by adapting the innovation in a way that conforms to the rules of the regime, or (2) through a restructuring of the regime that surrounds the niche.<\/p>\n Shared machine shops constitute a new environment for exploration in various fields of technology- and design-related topics that, compared to the mode-1-laboratories mentioned above, reveal unique properties: Since these workshops are typically organized around community-based principles (one has to consider TechShops as a commercial exception here), participation depends rather on common interests, shared values, and intrinsic motivation than on disciplinary boundaries and professions. Following this approach, shared machine shops offer new opportunities for collaboration and co-operation among heterogeneous actors that contribute their particular expertise and visions to any given context of shared interest. This often causes creative friction, which may either lead to small-scale inventions that serve the personal needs of its inventors, but in some cases also fosters solutions that could gain innovative momentum outside the shared machine shop, and beyond the initial motivations of the actors involved. Based on a secondary analysis of two different inventions which have their origins in hackerspaces and FabLabs, it shall be shown how this particular background has shaped the path for these inventions. Rather than making strong empirical accounts, this analysis illustrates how the notion of real-life laboratories serves as a fruitful concept to explain the distinct settings and constellations in the sketched niches for innovation. Both, the case of low-cost-prosthesis as well as the one of Makerbot recently gained some broader public interest. They represent examples for a whole bunch of inventions developed in SMS and show some of their most significant material traits and organizational backgrounds.<\/p>\n The first case we want to introduce as an evidence for the conceptual aim of this paper is the one of \u201clow-cost prosthesis\u201d. Building on a collaboration between Amsterdam\u2019s FabLab, the Indonesia-based House of Natural Fiber (HONF), which is a media and art laboratory in Yogyakarta as well as its associated FabLab (the \u201cHONFablab Yogyakarta\u201d), this project incorporates the principles of the FabLab Charta quite perfectly as it really draws on networking among different Fab-Labs, open knowledge sharing, and free access to community resources (http:\/\/fab.cba.mit.edu\/about\/charter\/). The general aim of the low-cost prosthesis project is to explore how a developing country like Indonesia can become self-reliant in building prostheses for the cost of about $50. The need for this endeavor is obvious (see: http:\/\/www.lowcostprosthesis.org): First, due to the increasing rate of amputations, there is an ever-growing demand for prosthetic limbs especially in developing countries where insufficient supplies of public health services often leads to diabetes, gangrene, and infection. Second, there are significant problems in providing prosthetics to people in need due to the high cost for readily available prosthetic limbs, and the lack of expertise, which is mandatory for proper constructing, fitting, aligning, and adjusting of prosthetics.<\/p>\n To offer a solution for this pressing problem, the low-cost prosthesis project started to develop a lower knee prosthesis by approaching an inclusive open innovation process, where end users, designers, researchers and manufacturers can contribute in a joint effort (Waag 2009). The current state of the project is reflected by a prototype of the \u201c$50 leg prosthesis\u201d (see fig. 1) which was developed in 2012 after several workshops with experts from various related fields (e.g. rehabilitation, biomechatronics, biomedical engineering, orthopedic technology, design etc.).<\/p>\n1 Setting the Scene: the nature of networked innovation<\/span><\/h2>\n
2 Laboratories in the Wild<\/span><\/h2>\n
2.1 Real-life experiments as a feature of innovation societies<\/h3>\n
2.2 Real-life laboratories: Niches for real-life experimentation<\/h3>\n
2.3 Shared machine shops as real-life laboratories<\/h3>\n
3 From real-life experiments to real-life innovations<\/span><\/h2>\n
3.1 The case of low-cost-prosthesis<\/h3>\n