And if Geospatial Data Infrastructures were fragmented and Splintering?
GDI as a socio-technical actor network; sociology of translation
GDIs encompass both technical and social elements. The question whether technology is primarily technical or primarily social has been extensively dealt with in the literature, notably in the realm of Actor Network Theory . Some view technology as socially constructed but technology certainly may have a profound impact on social relations as well. Hence, concrete instances of technology can be considered as socio-technical systems; truly heterogeneous systems where nor the technical nor the social dominate the other. The term ‘socio-technical system’ reflects our common understanding that both technique and social actors are prerequisite for ‘technology’ being meaningful. This is obviously the case for information and communication technologies (ICTs) like GIS-technology. Similarly, some view GIS both a technique and social relation .
The social construction of technology brings with it that concrete instances of technology emerge in a continuous process and interplay between human and technical actors. It also implies that that ‘technology’ is a dynamic rather than a static concept. Moreover, the (social) construction of technology implies a process of continuous negotiations where different actors attempt to influence the courses of action open to other actors. Michel Callon has coined this process ‘sociology of translation’ (which is synonymous for Actor Network Theory) . It is the process of creating alignment amongst potential allies and comprises four stages:
- problem (re-) definition. Some actors work together to define a problem so that other actors recognise it as their problem too;
- locking allies in. Actors lock allies into collaboration; some actors become indispensable to others;
- defining new roles. New situation is achieved through various mechanisms (persuasion, threat, inducements, etc.) and old network may dissolve;
- sustenance. Sustenance of the new situation is achieved; or, at least, relative sustenance.
Actors may be both human and non-human. (The latter comprise nature and technical artefacts.) Actors interact with other actors and in doing so, they put intermediaries into circulation, like texts, maps, aerial photographs, technical artefacts, human beings (skills, knowledge), or money. They define one another in interaction – in the intermediaries they put into circulation. Actors could be intermediaries themselves and eventually may be simplifications of networks (‘black boxes’). Some actors may become indispensable to other actors and/or intermediary between networks (‘obligatory point of passage’). Boundary objects help actors from different networks (or communities) to interact by providing shared understanding. To this end, boundary objects must be adaptable to different viewpoints on the one hand and robust enough to maintain identity across different sites on the other . Boundary objects are acknowledgement and discussion of differences between communities that enables a shared understanding to be formed. While the loose definition of a boundary object is helpful in initially bringing groups together, this looseness can also be detrimental. Ultimately, the different communities may interpret boundary objects differently.
Actor Network Theory may help in identifying conditions for relatively stable GDIs . It should be remembered, however, that GDIs like all socio-technical constructs are subject to continuous ‘translation’ between heterogeneous actors and, hence, are – and remain –potentially unstable. Nevertheless, and all other conditions remaining equal (ceteris paribus), simplification of a GDI by ‘translation’ or negotiation may reduce arguments and conflicts on loopholes and loose ends. Addressing and defining only a limited number of possibilities and keeping some ‘black-boxes’ closed can achieve this. On the other hand, multiple and redundant interactions between constituent actors may diminish the dangers that are associated with strong “obligatory points of passage”. This also implies that political support is necessary for sustained GDIs but no single predominant and powerful actors.
The tragedy of GDIs that do not provide geospatial information
GDIs are supposed to support and be utilised by a wide group of actors; collectors, processors, providers and users of geospatial data. However, each individual actor will be inclined to recognise that often “information is power” and, consequently will be reluctant to share that information with other actors. As a result, the ability of GDIs to provide information may seriously be curtailed. In general – and as we have seen this before – actors’ self-interests may be detrimental to the promises of GDI. In other words; self-interest may be at odds with common interests. This general theme was addressed by Garrett Hardin (1968) arguing that users of common-pool resources (“commons”) are caught in an inevitable process that leads to the destruction of the resources on which they depend. In this respect, GDIs might be fruitfully viewed as a particular instance of common-pool resources. Indeed, we may well call it “tragic” if GDIs would inevitably not be able to fulfil their (technological) promise if not their very reason of existence.
One way of coping with this “tragedy of the commons” is to strengthen central control. However, this would not only mean that the very characteristic of a “commons” might be jeopardized. It also might contradict contemporary trends of “good governance” as these are discussed in academic and professional circles. However, these dilemmas might also be dealt with in a different way than by central control. Empirical evidence has shown that their users can effectively manage local-scale common-pool resources for very long periods of time. Local communities apparently were able to craft appropriate institutions. The work of Elinor Ostrom and associates at Indiana University has produced several concepts and approaches that may also be useful in our present discussion on the effectiveness of GDIs . For example,
- polycentric arrangements. Multiple centres of power (and jurisdictions) may better reflect diverse preferences and values and could make better use of local knowledge and practices. Polycentric institutional arrangements may achieve order and high performance through competition as well as cooperation. Some degree of redundancy may reduce the danger of failure for the entire system;
- self-organisation. To be effective, collective-choice rules must be institutionalised. This, in turn, requires some degree of self-organisation.
- coproduction. Production of all services involves some active input from consumers as well as from the producers of these services. For example, medical doctors and patients together constitute healthcare.
An approach to GDI that allows for distributed jurisdictions, some degree of redundancy and self-organisation, and active involvement of all actors may contribute to its effective and sustained operations and, hence, to its success.
GDI as complex adaptive system; ability to learn
GDIs like other socio-technical constructs operate within a highly unstable environment. Their ability to adapt is key to their sustainability. In this respect, GDIs are complex adaptive systems. Adaptation to evolving circumstances, in turn, implies not only the ability to learn but specifically also the ability to learn how to learn . Learning may address needed adaptation to internal as well as external circumstances.
Learning is social and comes largely from our experience of participating in daily life. In complex adaptive systems learning may occur through informal and loose associations for sharing experiences, insights and information about the operation and performance of the system. This kind of associations (or groups) has become known as a ‘community of practice’ . A community of practice have three distinguishing characteristics. First, communities of practice are obviously formed around a shared practice that matter to people. More than an interest, members are able to develop a shared collection of resources: stories, experiences, tools, best practices, and so on. Second, communities of practice emerge around expertise rather than bureaucratic hierarchy. Third, communities of practice are only responsible to their members. In this sense, they are fundamentally self-organizing systems. Communities of practice are able to influence the ‘host’ systems in which they are implicated through the development and maintenance of social capital among community members. This social capital, in turn, mobilises and facilitates the ability for the host system to learn and to adapt to (changing) circumstances . It would follow that the sustained performance of those complex socio-technical systems like GDIs will be facilitated by informal groups of involved actors (producers, operators and users) with high degree of mutual trust rather than by formal, hierarchical and bureaucratic arrangements.