Part IV Social Science and Network Theory

Part IV Social Science and Network Theory

184 Social Science and Network Theory In previous chapters we have outlined the network theory of knowledge, and in particular its application to natural science. We now consider its implications for social science. Generally speaking network theory is permissive with respect to social science. It tells us little, for example, about the kinds of models that might best be utilised in exploring social science problems. This is because, as we noted in Chapter 8, it is essentially descriptive rather than prescriptive. It only becomes prescriptive when a theory under scrutiny breaches what it takes to be a realistic theory of knowledge. We have already seen at least one case of this: since it is a materialist theory which suggests that people direct ideas rather than ideas directing people (Chapter 14) alternative idealist conceptions of the role of knowledge are seen as unrealistic. In the chapters that follow we consider a number of other prescriptive consequences for social science practice. We start by looking at those theories of knowledge which distinguish between good and bad knowledge in a way that is fundamental to their explanatory strategy. It is important to be clear about what is being intended here. We are not suggesting that it is impossible or inappropriate for social-science investigators to make judgements of truth and falsity in their roles as citizens. Naturally we all do this. Neither are we suggesting that no judgements are appropriate in a professional social science context -we are, after all, pursuing what we take to be certain prescriptive implications of the network theory and the latter follow naturally from the adoption of a particular account of the way in which knowledge is generated. Rather, we are suggesting that, as a result of having adopted network theory, those alternative accounts for the generation of knowledge which assume good knowledge is to be explained in a manner that differs from bad knowledge, should be rejected as unrealistic. Naturally this prescription depends upon a commitment to network theory. Ultimately it cannot be demonstrated that alternative prescriptive accounts are definitively wrong. Indeed one of the virtues of the network theory is that it predicts such demonstration to be impossible. Nevertheless it is worth detailing the reasons for refusing to make the customary explanatory distinctions between good and bad, true and false, and modern and primitive. The first, which we have been arguing in previous chapters, concerns the workability

Social Science and Network Theory 185 of network theory. Our view is that it really does account for what is at present known about the growth of knowledge. But second, and this is important for the social science prescriptions that follow, it does so in an economical manner by finding general principles that explain the growth of all knowledge, whether good or bad, in the same way. Parsimony is, of course, a matter of taste. Despite the various difficulties that we have suggested stand in the way of such a programme, there is nothing per se impossible about providing different types of explanations for 'objectively' true and false knowledge. On the other hand, we believe that there is value in pushing general and parsimonious explanatory principles as far as they will go and it is the implications of this preference that we explore in the chapters that follow. Our approach is, then, broadly consistent with what David Bloor has called the 'strong programme' of the sociology of knowledge. 1 The strong programme has, he suggests, four tenets. (1) It is causal, concerning itself with the factors that generate knowledge. (2) It is impartial, that is, it assumes that both truth and falsity (or other such pairs of opposites) require explanation. (3) It is symmetrical, assuming, as we have been suggesting, that the same kinds of factors explain both true and false knowledge. (4) It is reflexive; since it aims at generality it explains its own derivation in terms of the same kinds of factors. We start with questions of impartiality and symmetry by looking at three areas of social science where there has been a persistent tendency to distinguish between the true and the false and then to explain these in different ways. The first such area is that of primitive thought. Here the 'manifest' inadequacy of native belief is often held to require an explanation different in kind to that accorded to our own scientific, and hence rationally explicable, knowledge. The second area is common sense. Again, this is often distinguished from science, and is held to be explained by different kinds of factors. The third such area is that of ideology. Here the argument is a little more complicated. Abandoning our predominantly empirical approach, we suggest that the assymetrical and the symmetrical have often co-existed in

186 Social Science and Network Theory the analysis of ideology, and we attempt to tease these apart in the work of Marx and Mannheim. We also note that as the impartial and the symmetrical gain ground, so the concept of ideology has a tendency to become general and lose its normative function. The network theory has strongly relativistic overtones that find their expression not only in the requirements of impartiality and symmetry, but also in that of reflexivity. In Chapter 21 we discuss the so called 'problem' of relativism and argue, against most present day views, that there is nothing self-contradictory in adopting a form of methodological relativism. Indeed such a stance is necessary if general and parsimonious explanations for the generation of knowledge are to be mounted. Various arguments against relativism -and notably the view that it is selfdefeating - are considered and shown to be incorrect. Next we turn to the question of verstehen. Verstehende sociologists have in general displayed a deep interest in understanding beliefs and actions in their own terms, and have often argued that it is inappropriate to import judgements of truth and falsity from other social contexts. There is thus an obvious similarity between the condition of impartiality and the practice of verstehende sociology. However, the commitment to impartiality is often coupled with the view that neither causal nor comparative analysis of alien belief-systems is appropriate. We consider this position and argue that network theory reveals both these options to be available to the social scientist, so long as realistic conceptions of 'cause' and 'comparison' are entertained. The widespread tendency of verstehende sociologists to reject these options rests, we suggest, upon a rigid but unrealistic conception of natural science. What, then, is different about social science if it is not the fact that the study of actors' meanings requires some entirely different and special approach? If, in other words, there is no basic conceptual distinction between the natural and social sciences? The answer to this question is easily seen in the context of network theory: the field of interests that lies behind and directs social science knowledge is much more obviously diverse than that which operates within certain branches of natural science. Furthermore this field of interests relates much more to a global concern with social legitimation than is typically the case in natural science. We explore these differences and their consequence -the fact that there is little or no consensus in social

Social Science and Network Theory 187 science. Our suggestion is, then, that social science is distinctive not because it is conceptually different per se from natural science, but because the social conditions of its production are very different. Finally, we turn to another vexed question -that of the relationship between social science and philosophy. Our argument is that for too long social science has been unduly influenced by the particular preoccupations of philosophy. We suggest that social scientists should stop doing 'misbegotten epistemology' and concentrate, instead, upon social science issues. Chapter 24 thus constitutes a kind of declaration of social science independence. 121 If network theory is realistic, then both those beliefs that are thought to be true and those taken to be false should be explained by the same kinds of factors. We should, therefore, be 'impartial' with respect to truth and falsity and 'symmetrical' in explanatory approach.