LaaN vs. Actor-Network Theory
Actor-network theory (ANT), also known as the sociology of translation or sociology of associations, proposes a socio-technical account that makes no distinction in approach between the social, the natural and the technological (Callon, 1986; Latour, 1996, 2005; Law, 1992). ANT is based upon the principle of generalized symmetry employing a single conceptual framework when interpreting actors, human and non-human. Latour (1996) writes "an ’actor’ in ANT is a semiotic definition -an actant-, that is, something that acts or to which activity is granted by others. It implies no special motivation of human individual actors, nor of humans in general. An actant can literally be anything provided it is granted to be the source of an action" (p. 370).
However, ANT has several limitations to be applied as a framework for dealing with complex learning environments. ANT explores the ways that heterogeneous networks of both human and non-human actors are constructed and maintained and focuses on tracing the transformation of these heterogeneous networks (Latour, 2005). Central to ANT is the concept of translation which is the process of creation of an actor-network and generation of ordering effects Law (1992). The main problem of ANT’s heavy emphasis on the construction, maintenance and transformation of actor-networks (i.e. translation) is that it reduces all actors into black-boxes, and thus ignores internal actions which are crucial for the creation of PKNs, and hence learning, such as seeing patterns, reflecting, (self-)criticizing, and detecting/correcting errors. These actions build the cornerstones of LaaN.
Moreover, according to Callon (1986), the translation process consists of four major steps: problematisation, interessement, enrolment, and mobilisation. However, the creation of knowledge networks cannot be dictated by a predefined process. It rather requires self-ordering and self-organization. Law (1992), while exploring the strategies of translation, acknowledges the self-ordering nature of knowledge networks. As he puts it: "translation is contingent, local and variable" (p. 387). In contrast to ANT, in LaaN, the creation of knowledge networks is rather undetermined, often unpredictable internal process within the knowledge ecology which is, by definition, a self-controlled, self-maintained, and self-organized entity.
Another limitation of ANT is that it does not distinguish between complex and complicated systems. ANT, particularly in Latour’s work, refuses to make the distinction between human and non-human actors i.e. between complex and complicated entities (Williams, 2007). ANT’s strong emphasis is on the heterogeneous nature of the actor-networks. Latour (2005) stresses that, in ANT, non-human actors, similar to human-actors, are treated as mediators rather than intermediaries. Latour makes a strong distinction between intermediaries and mediators. He writes: "For intermediaries , there is no mystery since inputs predict outputs fairly well: nothing will be present in the effect that has not been in the cause ... For mediators, the situation is different: causes do not allow effects to be deduced as they are simply offering occasions, circumstances, and precedents. As a result, lots of surprising, aliens may pop up in between" (pp. 58-59). Treating non-human actors as mediators makes from them complex entities where cause and effect are intertwined and cannot be separated. Non-human actors, however, are rather complicated entities - or in Latour’s terms intermediaries. All the components of a non-human actor are knowable and cause and effect relationships can be predicted. In LaaN non-human "actors" are just an enabler. At the heart of LaaN lie individual learners and their PKNs.
Another point where it also becomes clear that ANT does not make a distinction between the complex and the complicated is the ANT’s concern with the way in which the social is constantly reconfigured, or in Latour’s words ‘reassembled’ (Latour, 2005). Law (1992) stresses that the core of the actor-network approach is "a concern with how actors and organisations mobilise, juxtapose and hold together the bits and pieces out of which they are composed ... and so turn a network from a heterogeneous set of bits and pieces each with its own inclinations, into something that passes as a punctualised actor" (p. 386). Latour (2005) uses the verb reassemble to describe the same effect. He specifies five major uncertainties (p. 22):
• the nature of groups: there exist many contradictory ways for individuals to be given identity;
• the nature of actions: in each course of action a great variety of agents seem to barge in and displace the original goals;
• the nature of objects: the type of agencies participating in interaction seems to remain wide open;
• the nature of facts: the links of natural sciences with the rest of society seems to be the source of continuous disputes;
• and, finally, about the type of studies done under the label of a science of the social as it is never clear in which precise sense social sciences can be said to be empirical.
Latour argues that if the social is based on layers of uncertainties, then the social needs to be reassembled. However, in the new knowledge intensive era, the relationship between different knowledge nodes or in Law’s terms ‘heterogeneous bits and pieces’ is becoming flexible and is changing rapidly; thus, it cannot be captured through a reconfiguration process. Reconfiguration, or in Latour’s terms reassembling, works well for complicated systems in which different components and associated relationships can be identified and managed. Reconfiguration, however, cannot work while dealing with complex knowledge systems comprising many interacting identities. In the latter case, networking is the solution. In complex knowledge systems, the way the knowledge nodes network with each other results in unpredictable movements in the knowledge ecology. Knowledge ecologies lie at the heart of LaaN.
Furthermore, Latour (2005) claims that "it’s possible to render social connections traceable by following the work done to stabilize the controversies" specified above (p. 16) and that the role of ANT is to trace actor-networks (p. 128). To note here that ’network’ in Latour’s vocabulary means something different. Latour points out that ’network’ is an ambiguous word meaning "a string of actions where each participant is treated as a full-blown mediator" (p. 128). To avoid this confusion, Latour suggests using ’work-net’ rather than ’network’. He writes: "Really, we should say ’work-net’ instead of ’network’. It’s the work, the movement, the flow, and the changes that should be stressed" (p. 143). In complex knowledge systems, however, there is no chance to trace social connections, nor is it possible to follow the actors or their actions. Latour himself acknowledges that following the actors themselves is not an easy task since, as he writes, "the actors to be followed swarm in all directions like a bee’s nest disturbed by a wayward child" (p. 121). Thus, there is no means to trace actors’ actions and connections because their actions are uncertain, unexpected, and often hidden; and their connections are varied, ubiquitous, and open. The role of LaaN is neither to follow actors nor to trace their actions or connections. Its major role is to help learners build and nurture their PKNs and to foster connections between different PKNs in order to form a complex, adaptive, dynamic, open, and living entity; i.e. a knowledge ecology.
References:
Callon, M. (1986). Some elements of a sociology of translation: Domestication of the scallops and the fishermen of st brieuc bay. In J. Law (Ed.) Power, Action and Belief. A New Sociology of Knowledge?, (pp. 196–233). London: Routledge & Kegan Paul.
Latour, B. (1996). On actor-network theory: A few clarifications. Soziale Welt, 47(4), 367, 369–381.
Latour, B. (2005). Reassembling the Social. An Introduction to Actor-Network- Theory. New York: Oxford University Press.
Law, J. (1992). Notes on the theory of actor-network: Ordering, strategy and heterogeneity. Systems Practice, 5(4), 379– 393.
Williams, R. (2007). Managing complex adaptive networks. In Proceedings of the 4th International Conference on Intellectual Capital, Knowledge Management & Organizational Learning, (pp. 441–452).
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