One further particularly challenging issue concerns the process of informing or ‘educating’ participants on the technical details of the issue in question.
Although the Seminar heard repeatedly of the positive capacities of nonspecialist citizens in assimilating complex issues ([J] and section 3.1 above), this does not mean that the handling of specialist knowledge is necessarily straightforward or unproblematic in participatory process.
Effective communication of scientific information – both within and beyond a participatory process – is an important field of expertise in its own right [L]. It requires close attention to the striking of an appropriate balance between specific and general pictures, between the background details and their practical implications and between contending points of view [L].
This in turn raises thorny questions over reconciling divergent but equally well-founded interpretations of ‘the facts’ [L]. It is at this point that we encounter a further series of neglected issues around the importance of appropriate public communication of uncertainty [H].
These questions apply as much outside, as they do within participatory process. Like the communication of science, expertise on the different forms and degrees of uncertainty is itself an important interdisciplinary field of expertise in its own right [K].
Although often treated merely as risk (under which probabilistic techniques are applicable), there exist many other forms of ‘incertitude’ to which these techniques are – by definition – not applicable.
These include various forms of complexity, which require elaborate modelling procedures and methods drawn from beyond the confines of any single discipline and presenting particular challenges in communication [G].
Beyond this, uncertainty in the strict sense of this term applies to situations under which the possible outcomes are well defined, but the probabilities remain unknown [G].
Ambiguity, by contrast, describes conditions where it is not the probabilities that are problematic, but the definition of the outcomes themselves [G]. Finally, the state of ignorance refers to a situation under which there exists imperfect information both on the probabilities and the outcomes – with consequent exposure to ‘surprise’.
Each of these states of incertitude apply as much to specialist as to wider public knowledge. It follows from this, that – whilst expert understandings provide an essential input – it should not be assumed that specialists in particular disciplines will automatically be in the best position to articulate the complexities, uncertainties, ambiguities or ignorance associated with their own disciplinary body of knowledge.
Indeed, it is often the case that these wider and more intractable forms of incertitude are systematically understated – and even concealed – in specialist communications with the public, media or policy making bodies [J].
It has already been mentioned in the introduction to this report (Section 1.2) – that much policy-relevant science rests on socially-informed assumptions.
This in turn means that many important uncertainties and ambiguities arise not just in the science itself, but also in relation to associated social conditions [H].
The existence of scientific uncertainty typically presents a degree of ‘interpretive flexibility’, which is then compounded by socio-political ambiguities [J].
In the end, it is clear that the pervasive role played by uncertainty in policy-relevant science, ensures that discussions in this area retain an irreducibly political – as well as scientific – character [J].
One quite distinctive perspective in this regard, emerges among some in the risk communication field. Concerns are sometimes raised here that – far from there being too little public acknowledgement – there is, in fact, too much public and policy attention to uncertainty [J].
Associated with this, is a concern over the existence of serious limits on the capacity of members of the general public properly to comprehend the more demanding applications of probabilistic analysis [J].
However, set against this is the fact that failures properly to understand the formal logic of probability are also well documented amongst many experts [J].
And there is the tendency for specialist disciplines themselves to over-apply probabilistic techniques to complex problems of uncertainty, ambiguity and ignorance under which probabilities are – by definition – simply not valid [G].
Accordingly, signs of public scepticism or
resistance to probabilistic reasoning may raise as many questions over the practical applicability and intrinsic value of these methods, as may be raised over public understanding [J].
This relates to the crucial question of framing discussed earlier (Section 3.2) – and the need to be sure of the
appropriateness, relevance and applicability of a specific body of science to a particular social or policy issue [J]
Taken together, these issues raise an important strand in well-rehearsed debates around the problems of the ‘deficit model’.
This refers to the presumption that understandings displayed by the general public are necessarily deficient in relation to specialist expertise [P].
In short, those with experience in wider applications of participatory exercises tend to be more sanguine on these issues than the view from the risk communication field mentioned above [J].
Where research has specifically addressed the issue of public understandings of ‘uncertainty’, a picture emerges not of confusion, but of considerable subtlety and sophistication [J].
The Seminar heard in particular detail from projects in the field of agricultural biotechnology on this score [J]. Indeed, contrary to the views of some in the field of risk communication, pan-European experience in this field suggests that there exists an important sense among the general public that issues of uncertainty are systematically underplayed and neglected – rather than overstated – in the communication of science and public policy [J].
In emphasising this crucial message, it is important to be clear that rejection of the ‘deficit model’does not imply that public understandings of scientific, technological or risk issues are always comprehensive, authoritative or robust.
The point is rather that no single body of specialist knowledge can claim such status either, and that diverse public knowledges hold an essential complementary role to formal expertise in achieving truly complete, authoritative and robust overall societal understandings.
Accordingly, rejection of the deficit model in no way diminishes the importance of careful attention to public education as part of participatory process.
And this in turn raises the serious but often neglected challenge to the effect that it cannot be assumed that participating individuals or organizations – any more than sponsoring institutions discussed earlier (Section 3.2.1) – will automatically display a commitment to the necessary communication and learning. Indeed, the dedication of the necessary time and effort may often be quite welcome or even actively resisted and requires equally careful attention [K].
Even where such efforts are successful, where a participatory process has achieved intensive learning on the part of those involved, questions may then be raised – precisely for this reason – over the representativeness of the final results as a reflection of external (less well informed) public perspectives [K].
Queries may also emerge over the value added over more direct engagement with the technical specialists themselves [K]. Moreover, intensive study and deliberation provide no guarantee of any increased disposition to qualities of ‘good citizenship’ such as empathy, altruism and willingness t ocompromise [G].
Indeed, higher levels of formal education may often be associated with exactly the opposite traits [G]. And there is a perennial concern that the more self-selecting forms of participatory process may serve simply to accentuate existing patterns of privilege already enjoyed by the most confident, educated and outspoken social groups [K].
Although a necessary and highly effective element in the social response, public engagement in itself offers no panacea to the challenge of social learning under uncertainty, ambiguity and ignorance.
This said, it is important to recognise that an appreciation of the substantive benefits of participatory process is founded on a rather more sophisticated understanding of the role of knowledge in governance, than the simple ‘deficit model’ suggested by the concept of ‘public education’ [P].
Under a ‘social learning’ perspective [N], by contrast, knowledge is seen more as a relational, action-oriented process, than as a static ‘off the shelf’ commodity that can be transferred to those in ‘deficit’ [P].
This recognises the fact that some of the most crucial understandings on any given policy question may be forms of ‘tacit knowledge’ – very different from the codified information deployed by professional experts and specialist disciplines [P].
This tacit knowledge addresses practical questions that arise in science governance, like: “where to look?”, “whom to ask?” “how to recognise quality?”, “how to reproduce results?”, “how to do?” [P]?
For instance, locality-specific knowledge concerning populations of fish and their predators, distributed collectively among sea fishers or bird watchers, has a complementary value to the more systematically organised and explicit bodies of knowledge represented in academic modelling for fish stock management [I].
Although often more authoritative on particular details, specialist disciplines may become blinkered by their own assumptions, and may be insufficiently sensitive to knowledges produced by other specialist disciplines, especially where these are in some tension with their own findings.
Participation addresses this by involving a diversity of specialist perspectives – including those of stakeholders. By also including non-specialists in the deliberation, it is often the case that crucial questions are raised and prioritised which might otherwise remain neglected.
In short, an understanding of the substantive benefits of participatory process resides in the recognition that forms of knowledge and modes of learning are each plural in nature.
As such, the value of engaging different knowledges lies as much in the mutual tensions and challenges as in the potential for coherent integration.
In an area as complex as the governance of science and technology, there exist no monopolies on salient knowledge and the domain of what is relevant cannot readily be reduced to a single formally codified scheme [P].
It is important to qualify this picture by acknowledging the dangers of taking the argument too far. Advocacy of participatory methods is sometimes associated (at least implicitly) with rather romantic reversals of the ‘deficit model’, under which ‘citizen knowledges’ are approached as being immune to deficiencies – or even in some sense superior to – specialist expertise [K].
The point is not that nonspecialists should or can ‘second guess’ technical expertise. Even with intensive attention to awareness raising, education and training as essential features of effective participatory process [Q], it remains the case that there exist real limits on the capacity of nonspecialists to acquire relevant expertise [K] (see Section 2.2.4).
The point is not therefore that interested stakeholders or randomly recruited members of the public can be better experts than the experts.
The issue is rather one of acknowledging the crucial role played by cultural values, sectional interests and political and economic power in the shaping of knowledge. Perhaps the best way to illustrate this, is to highlight the role of public engagement in posing the questions posed of science and interpreting the answers.
Who asks these questions? Who makes the assumptions? Which research is funded? Who is accredited to perform it? How are results communicated? Who interprets the answers? Who is informed and at what stage?
What is stated and what left unsaid? Which knowledge is held privately and subject to proprietary rights and which is placed in the public domain [P]? All such factors are significantly influenced through the exercise of power.
Discussion over these issues is therefore intrinsically political in nature – and so more readily understood as a legitimate matter for public engagement.
The unique contribution of scientific method and technical expertise thus remains acknowledged, but is conditioned by recognition that it remains influenced and framed in subtle ways by these broader societal factors.
The role of public engagement, then, is to address the precise implications of these broader societal factors. In practice, this means that the practices and methods of science remain at centre stage, but move from being seen as definitive repositories of knowledge towards more nuanced roles as set of disciplines for social learning: ensuring greater rigour and accountability in the governance of science [L].
One further complication in this picture, concerns the role of transparency. The problem here, is the fact that clarity on one dimension typically requires the obscuring of another [P].
Transparency over detailed understandings of pollutant distribution at a particular static point in time, for instance, can conceal the dynamic ways in which this picture changes over time.
In general, transparency over the detail of what is known can impede communication of what remains unknown. Likewise, transparency over the detailed nuancing of stakeholder positions, can obscure the ‘big picture’ basis for common ground and closure.
Naïve claims or aspirations to ‘full transparency’ may ironically (just like simplistic appeals to ‘expertise’ or ‘truth’), actually prove to be obstacles to effective social learning [P].
It is against this background that participatory process can be understood as a natural complement to – rather than a contrast with – conventionally practised science.
In the spirit of scientific enquiry itself, participation offers a means to ‘open up’ the grounding and conditionality associated with different forms of knowledge [P].
It offers a means more rigorously to validate the nature and origins of the values, which underpin the assumptions or interpretations adopted in research [Q].
The key challenge is therefore how to integrate specialist expertise and non-specialist knowledges – each conditioned by their associated interests and values – in a fashion that promotes the most effective social learning.