3. Knowledge

To imagine universities as open knowledge institutions, we begin with some words about forms of knowledge, leading to ways in which knowledge has been conceptualised as a good in the knowledge economy.
3. Knowledge
·
Contributors (13)
& 8 more
Published
Mar 08, 2019

3.1 Types of Knowledge

We advocate for universities as Open Knowledge Institutions which institutionalise diversity and, working with the broader community, generate a common pool resource of knowledge. To imagine universities as open knowledge institutions, we begin with some words about forms of knowledge, leading to ways in which knowledge has been conceptualised as a good in the knowledge economy.

Information is everywhere. On the basis of its building blocks, we may develop knowledge. Occasionally, some forms of knowledge will be accorded the status of wisdom. Education deals with existing knowledge and its transmission, but research institutions value new knowledge. To be worthy of a research degree, students are required to make 'a genuine contribution to knowledge.' The skills required to produce that contribution are highly prized in themselves, as well as in their direct or indirect applications. Culture is essential in determining what knowledge means and who gets to share it (Hartley, 2018). One culture’s knowledge may be another’s cultural noise, proving to be meaningless or meaning something quite different in other cultural contexts. Most cultures, however, distinguish formal, certified forms of knowledge – sanctioned by publishing houses, libraries, or national institutions – from informal, unsanctioned forms of knowledge, the most ubiquitous evidence of which can be found in today’s social media.

Knowledge is global. This sometimes brings different knowledge systems into conflict. Different levels within a knowledge system also come into conflict, for example, when the micro (agent/text), meso (institution/discourse) and macro (system/network) levels interact. As information became globally instantaneous with the advent of the telegraph, knowledge too took on more universal characteristics. But as the proportion of populations sharing in that knowledge has grown and accelerated in the last half-century, knowledge has tended towards a distinctive bifurcation into 'violent-productive' knowledge in contrast to 'tribal-connective' knowledge. Hartley distinguishes 'deep, specialist, expert, disciplinary and literate' productive knowledge, claimable as intellectual property, from connective knowledge, which is 'broad, circulating in everyday language and popular culture, open to everyone' (Hartley, 2018).

While the productive, sanctioned, knowledge might once, in Western tradition, have been written in Latin, the connective type was once only orally communicated in the local vernacular. Now, in the age of multimedia, all knowledge types appear across all communication forms. From the perspective of institutions of learning, a huge and unresolved question is how much, and how consistently, unsanctioned (connective) knowledge is relevant to the role of formal (productive) learning or discovery. How much should it be embraced, or excluded? This question is particularly challenging for an aspiring Open Knowledge Institution with strong foundations in local communities extending beyond the existing carefully selected elites.

Another way of thinking about knowledge is to distinguish between know-how and know-what. Know-how is often less sanctioned and formal, and not a basis of knowledge in traditional universities. Know-what is seen as the basis of scientific knowledge: the building blocks of the scientific method. Indigenous knowledge can present fundamental challenges, both in terms of methodological compatibility but also in terms of inclusion or exclusion of audience.

In this chapter, 'knowledge' is presented in various methodological interpretations, and particularly from scientific, economic, systemic and cultural perspectives. While these views do not necessarily coalesce, they present the potentialities and limitations of open knowledge and open knowledge institutions in several different lights.

3.2 Knowledge as a Good

Knowledge is often imagined as an economic good, whereby it is accorded a certain type of value. In particular, it is seen as production, whose inputs can be put to other uses and which requires coordination of production. The quality of the organisation (i.e., the governance and infrastructure of production) and translation of these inputs into outputs are the basis of the economy of knowledge production. There are several institutions engaged in this production, though they differ in how these economic goods are conceived: variously as private good, public good, club good and common pool resource.

The production of knowledge is often, mistakenly, thought to be a public good. It is certainly true that new knowledge does have characteristics of a public good, in the technical economic sense of that term. That is, knowledge is expensive to hold exclusively and does not lessen in value when shared. However, new knowledge also has characteristics of a private good, where exclusion can be created through secrecy, for instance, by simply not telling anyone, or as a club good, wherein members of privileged groups (e.g., medieval guilds, or industry associations) are able to maintain ownership of valuable knowledge.

Notions of ownership often serve as organisational mechanisms in economies of knowledge; for example, markets which are organised around intellectual property rights or the commercialisation of knowledge. These are most easily observed in terms of corporate laboratories which are organised primarily around the exploitation of knowledge for financial gain. Private organisations, which operate with knowledge as a private good, are typically controlled through public regulation. Club goods, such as consortia of research organisations, also rely on governance mechanisms to control access to the 'good', i.e., knowledge.

Knowledge can also be organised through the commons, in which knowledge is considered a common pool resource (Ostrom, 1990). Knowledge, understood as a common pool resource, is different from knowledge as a private or public good, thus transcending purely economic categories. In a common pool resource, a community comes together to create rules of governance (institutions) for the creation, maintenance, and use of the common pool resource based on mutually shared values and moral commitments. This works through community-created rules, not through legislation, regulation and public fiscal funding (as in a public good), or through markets and hierarchies (as in a private good). Common pool resources are often difficult to create, because governance is hard, but are often the most efficient institutional form for the production and consumption of goods under a wide variety of circumstances, superior to both public and private forms of governance.

Approaching knowledge as a common pool resource differs from the economy of knowledge in terms of the role of public interest, as embedded into a community of knowledge producers and users. The theory of social choice has shown that there is no way to aggregate individual interests consistently into one single proposition of public interest. In fact, this requirement would imply that knowing the public interest results in systemic closure. This dilemma has been succinctly analysed by Amartya Sen (2009) in his Idea of Justice, where he provided the systematic reasons why the public interest can only be defined via a process of deliberation that is as inclusive as possible, and that entails a process of forming and transforming individual interests via an open public discourse.

However, as Sen also has shown, in this context, the challenge emerges whether we define ‘public’ in the context of local and national societies or establish a global reference frame. A deliberative process in a rich country might result in the institutional choice to declare the physical comfort of elderly citizens as a public interest (perhaps including certain public national healthcare services), while at the same time ignoring pressing public health issues in poor developing countries. This problem has been highlighted by global philanthropic initiatives to support research on malaria, for example. This suggests that a system of producing knowledge as a common pool resource must be grounded in an open public dialogue about the adequate forms of institutionalising it within a specific domain, such as certain areas of public health, pharmaceutical research and medicine. This requirement is, in turn, based on the idea that the system itself is in the public interest.

This assumption is a value proposition that needs to be put into the perspective that openness of knowledge can also be a private economic good: this is often the case in fields of technological innovation where network externalities loom large (e.g., Tesla opening access to large segments of its patents). This does not mean that less access would be in the public interest either. The more fundamental question is whether institutional forms of economisation of any kind, private and public, are in the public interest, or whether knowledge should be produced as a common pool resource.

This question is especially virulent if we consider different forms of knowledge. Economisation is often the default option regarding all forms of productive knowledge (the sciences, medicine, engineering, and so on). Other kinds of knowledge may not be susceptible to economisation in a principled way, because they relate to the formation of identities, create cultural values, or enable various ways of resolving conflicting worldviews. In terms of disciplinary institutionalisation, these are the humanities in the broadest sense, as well as many variants of social science. Since economisation cannot apply, the adoption of a generic economic view on knowledge needs to be subject to considerations of political public interest. But even when considering productive knowledge, there are important issues of implicit and explicit value dimensions of research (such as in medicine) or disciplinary cultures (such as economics vis à vis other social sciences).

In summary, the economic analysis of knowledge and knowledge systems always needs to be embedded into considerations of public interest. That is, it needs to be organised as an open process of generating the knowledge about the public interest.

3.3 Open and Closed Knowledge Systems

Knowledge systems can be either open or closed. 'Open' and 'closed' are words with moral orientation: an open mind signals enlightenment and charity, whereas a closed mind is dogmatic and obstinate; an open door points to the future, but shady dealing happens behind closed doors; an open economy drives wealth and prosperity, a closed economy is run by dictators.

The value of  'open' is not a moral imperative but a property of a system that seeks to thrive in a changing, uncertain world: where not everything is known or can be known a priori about the world, its possibilities and prospects. Open is a way of being, for an agent or any complex system, that is adapted to an environment of change (Kauffman, 1993). This vision of what it means to be open reflects the principles of thermodynamics (Prigogine and Stengers, 1984), in which to be open is an evolutionary strategy, albeit a costly one.

An open system and a closed system can both survive in an unchanging world, but a closed system will do better because it will optimise according to efficiency. In a changing world, a closed system is fragile, whereas an open system is robust and adaptable. An open system can grow and evolve. This trade-off, between cost and dynamism, is a key distinction between open and closed systems.

The reason 'open' has such positive emotional valence to the modern ear is that the essence of modernity is to live in a continually changing world. Indeed, to be modern is to attain stability and poise, to thrive, amidst accelerating change and mobility. This is the world of new technologies, of economic growth and globalisation, of cultures merging and societies evolving. Knowledge (not just data but also meaning) is becoming increasingly networked. The technological landscapes within which knowledge is created are characterised by accelerating rates of change. This is creating challenges for all players in the system: raising questions of privacy and control, transparency and accountability; as well as creating new opportunities and ways of engaging with the products and the building blocks of new knowledge.

Many of our responses to problems associated with increased rates of change are to attempt to close down the sharing of knowledge (for example by such means as Digital Rights Management software applied to digital publications); to privatise and commercialise commons such as data (a recent example being the 2018 Facebook and Cambridge Analytica data scandal); and to increase regulation and censorship of the internet (for example, China’s Great Firewall). Closure becomes the answer to changes, instead of increased transparency and openness. However, this is a fundamentally flawed response. Closed systems are more fragile in a world of accelerating change. Seeking to harden them with regulatory stiffening only delays inevitable reckonings. 

Institutions that might operate as either open or closed knowledge systems (OKS and CKS respectively) include government, funding organisations, disciplinary associations, journals, Internet platforms, and so on. A key distinction between open and closed knowledge systems relates to the boundaries that exist between knowledge and non-knowledge. In CKS, the borderline is rigid and the process of boundary-making is top-down. Disciplinary boundaries and structures are also fixed. In OKS, the border among knowledge and non-knowledge is endogenous to the interactions between all elements in the system. In CKS, for example, Indigenous knowledge may be declared as non-knowledge because it does not fit comfortably within the established disciplinary order; in OKS users may be involved in integrating Indigenous knowledge into the knowledge system. Further, in OKS, disciplinary borders are porous and always open to negotiation. In general, closed knowledge systems are hierarchical and operate as top-down, placing an emphasis on control and governance.  

Figure 2a. Open Knowledge System 
Figure 2a. Open Knowledge System 

Figure 2b. Closed Knowledge System
Figure 2b. Closed Knowledge System

These two diagrams are conceptual maps of systems that depict simple and ideal-typical structures and elements. In practice, the systems are actualised via processes of institutionalisation. The university is one, and presumably the most important, form of institutionalisation embedded into other forms, such as the journal system, disciplinary organisations, or government institutions. The systems can be realised as fractals on different levels: for example, disciplines manifest a similar structure and can be analysed within the same framework. In this case, it is important to recognise that there is potential for conflict and coordination failure: for example, the controlling instances of disciplines may stay in competition with other disciplines, so that incentives for closure evolve endogenously. This, in turn, raises the question of how the’ ’relationship between different systems manifestations is governed.  Universities may, for instance, be involved in governing the interaction among disciplines within their jurisdiction, without, however, being able to determine the borders among disciplines on a global level.

One fundamental difference between CKS and OKS is the role of 'consumers' (users) of knowledge. Whereas in CKS, experts also govern the use of knowledge (e.g. a doctor ordering a therapy, literary critics recommending books), in OKS, the consumers become involved in the use of knowledge (e.g. patients have a say in selecting non-standard therapies; readers recommend good books to each other in Goodreads and social media). In addition, there is a feedback loop between use and knowledge. In CKS, use may also lead to knowledge-creation on the part of users (such as tacit knowledge in implementing technologies: Mokyr, 2009), but in OKS, this is explicitly fed back into the production of knowledge, for example in citizen science or in fan co-creation of mobile phone designs in Xiaomi (Shirky, 2015).

In CKS, students have access to knowledge but are subject to hierarchical governance structures (for example, learning is restricted to the disciplinary framework; or teaching is restricted to what is examined, not what is known). Access combines with a passive and restricted role in knowledge acquisition. In OKS, students become producers of knowledge and are active in determining the content of learning. In a similar vein, consumers gain access to knowledge (for example, modifications of knowledge are less impeded by copyrights and patents). Other differences in access include publication practices (for example, open access in OKS).

In CKS, resea rc h e r s a r e s u b j e ct to control from abovesuch as performance evaluation and limited term contracts, with research aims determined by higher-level decision makers). Often this is institutionalised in an indirect way via the organisation of disciplines: peer review and journal organisation, for instance, may restrict freedom in determining the choice of topics and methods, since incumbents control the process (researchers who have a prominent position at the control level). In OKS, researchers enjoy much more freedom, and are more directly involved in the governance process (for example, peer review may be transparent and public, allowing for responses to reviewers).

Both open and closed systems exist in complex and dynamic networks characterised by constant interactions between internal and external actors, which all shape the ways these systems operate and can be governed. There are more actors involved in knowledge systems, with various intermediaries such as libraries and curators, and the beneficiaries of research outputs who might not directly interact with major components in the diagrams, such as patients benefiting from medical research.    

Under some circumstances, openness becomes problematic, or even deeply negative. Common critiques of the 'information deluge', prompted and supported by the openness of the Internet, highlight the challenges in open knowledge. Too much open knowledge may become chaos and noise; if many knowledge objects are released in an unmediated or uncoordinated form, the results may lessen the value and impact of research and knowledge. Open may also be dangerous when it extends knowledge into places where it may cause harm, such as terrorism, when it exposes personal information (such as medical records), or when it takes advantage of inequitable relationships (notably, Indigenous knowledge). Even if the 'good' (or commodity that is sold, traded, protected, or consumed) may offer a benefit for some, it may do harm to others. Therefore, open knowledge must be embedded in systems which foster exchange and value heterogeneity to promote transparency for the public good.

Comments
10
Anita de Waard: This is a step too far/fast, for me, as a physicist :)!
Cameron Neylon: I think this comment got unmoored from its place. I can’t find the bit you’re referring to but I assume its the ‘Knowledge’ chapter and its quasi-physical analysis?
Anita de Waard: At this point I find I need a definition of an ‘open’ system, and how the current system is ‘closed’?
Cameron Neylon: Point taken. I think this chapter needs work on refining our terminology usage to be more consistent and rigorous.
+ 1 more...
Anita de Waard: This is not an obvious example, please add reference or explain
Cameron Neylon: The concept here is the idea that where network effects are significant that there are benefits in opening up intellectual property and other restrictions. One case of this is Tesla openly licensing its patent portfolio (https://www.tesla.com/blog/all-our-patent-are-belong-you). Even though this was received in some quarters with some cynicism (see e.g. https://insideevs.com/teslas-opening-patent-portfolia-seen-largely-pr-move-little-significant-impact/) it illustrates that the traditional notions of where value arises are not always clear in the presence of substantial network effects.Other examples we should perhaps include are the release of compound data by pharmaceutical companies and some pre-clinical trial and research data. There are also examples from mining where data-sharing is common because the network effects are large and the direct competitive losses are small.
Anita de Waard: I’d recommend making reference here to Stephan’s work on the economics of science. https://www.amazon.com/Economics-Shapes-Science-Paula-Stephan/dp/0674088166
Anita de Waard: This is quite a bold statement: on the one hand it presumes education = knowledge transfer (which can certainly be debated) and on the other, that ‘new’ and ‘old’ knowledge can be identified for any group of people: also not an irrefutable claim!
Cameron Neylon: There are two levels to this. One is a statement of practice - that education is about existing claims/knowledge and universities place value on novelty. The other is a more fundamental statement. I’m not sure we need to make the stronger statement at this point, so maybe re-wording to make it clear as an observation of practice at this point? eg if we switched knowledge for ‘information’ in this sentence