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I. Introduction

The organization of societies and states is predicated on the development of Information Technology and has begun to enable the construction of specialized networks. These networks aid in the mobilization of resources on a global platform.[1] There is a need for governance structures that embody this globalized thinking and adopt superior information technology devices to bridge gaps in the operation and participation of not only political functions but also economic processes and operations.[2] Currently, public institutions fall short of an optimum level of functioning simply because they lack the information, know-how and resources to respond effectively to this newly globalized and economically liberalized world order. Civil society is beginning to seek a greater participatory voice in both policy making and ideating, which require public institutions to institute a method of allowing this participation while at the same time retaining the crux of their functions and processes. The network society thus requires, As argued by Castells, a new methodology of social structuring, one amalgamating the analysis of social structure and social action within the same overarching framework.[3] This Network propounds itself as a 'dynamic, self-evolving structure, which, powered by information technology and communicating with the same digital language, can grow, and include all social expressions, compatible with each network's goals. Networks increase their value exponentially through their contribution to human resources, markets, raw materials and other such components of production and distribution.' [4]

As noted by Kevin Kelly,' The Atom is the past. The symbol of science for the next century is the dynamical Net.…Whereas the Atom represents clean simplicity, the Net channels the messy power of complexity. The only organization capable of nonprejudiced growth or unguided learning is a network. All other topologies limit what can happen. A network swarm is all edges and therefore open ended any way you come at it. Indeed the network is the least structured organization that can be said to have any structure at all. ..In fact a plurality of truly divergent components can only remain coherent in a network. No other arrangement - chain, pyramid, tree, circle, hub - can contain true diversity working as a whole .'[5]

A network therefore is integral to the facilitation, coordination and advocacy of different agenda within a singular framework, which seeks to formulate suitable responses to a wide range of problems across regions. An ideal model of a network would therefore be one that is reflective of the interconnectivity between relationships, strengthened by effective communication and based on a strong foundation of trust.

The most powerful element of a network is however the idea of a common purpose. The pursuit is towards similar ends and therefore the interconnected web of support it offers is in realization of a singular goal,

II. Evolution of the Network

There are certain norms that must be incorporated for a network to be able to work at its best. Robert Chambers, in his book, Whose Reality Counts? Identifies these norms and postulates their extension to every form of a network, in order to capture its creative spirit and aid in the realization of its goals.[6] A network should therefore ideally foster four fundamental elements in order to inculcate an environment of trust, encouragement and the overall actualization of its purpose. These elements are; Diversity or the encouragement of a multitude of narratives from diverse sources, Dynamism or the ability of participants to retain their individual identities while maintaining a facilitative structure, Democracy or an equitable system of decision making to enable an efficient working of the net and finally, Decentralization or the feasibility of enjoying local specifics on a global platform.[7]

In order to attain these ideal elements it is integral to strengthen certain aspects of the practice through performing specific and focused functions, these include making sure of a clear broad consensus, which ensures the co-joining of a common purpose. Additionally, centralization, in the form of an overarching set of rules must be kept to a minimum, in order to facilitate a greater level of flexibility while still providing the necessary support structure. The building of trust and solid relationships between participants is prioritized to enhance creative ideation in a supportive environment. Joint activities, more than being output oriented are seen as the knots that tie together the entire web of support. Input and participation are the foremost objectives of the network, in keeping with the understanding that "contribution brings gain". [8]

Significant management issues that plague networks include the practical aspects of bringing the network into function through efficient leadership and the consolidation of a common vision. A balanced approach would entail a common consultation on the goals of the network, the sources of funding and an agreed upon structure within which the network would operate. It is also important to create alliances outside of the sector of familiarity and ensure an inclusive environment for members across regions, allowing them to retain their localized individuality while affording them with a global platform. [9]

III. Structure

The structural informality of a network is essential to its sustenance. Networks must therefore ensure that they embody a non-hierarchized structure, devoid of bureaucratic interferences and insulated from a centralized system of control and supervision. This requires an internal system of checks and balances, consisting of periodic reviews and assessments. Networks must therefore limit the powers of supervision of the secretariat. The secretariat must allow for the coordination of its activities and allocate appropriate areas of engagement according to the relative strength of the participating members.

One form of a network structure, postulated within a particular research study is the threads, knots and Nets model. [10] It consists of members within a network bound together by threads of relationship, communication and trust. These threads represent the commonality that binds together the participants of the particular network. The threads are established through common ideas and a voluntary participation in the process of communication and conflict resolution. [11]

The knots represent the combined activities which the participants engage in, with the common goal of realizing a singular purpose. These knots signify an optimum level of activity, wherein members of the network are able to support, inspire and confer tangible benefits onto each other. The net represents the entire structure of the network, which is constructed through a confluence of relationships and common activities. [12] The structure is autonomous in nature and allows participants to contribute without losing their individual identities. It is also dynamic and flexible; incorporating new elements with relative ease. It is therefore a collaboration which affords onto its members the opportunity to expand without losing its purpose. The maintenance of such a structure requires constant review and repair, with adequate awareness of weak links or "threads" and the capability and willingness to knot them together with new participants, thereby extending the net.

For example, the Global Alliance for Vaccines and Immunization used a system of organizational "milestones" to monitor the progress of the network and keep the network concentrated. It requires a sustained institutional effort to fulfill its mandate of "the right of every child to be protected against vaccine-preventable diseases" and brings together international organizations, civil society and private industry. [13] As postulated within the Critical Choices research study of the United Nations, clearly defined milestones are integral to sustaining an effective support mechanism for donors and ensuring that all relevant participants are on board. [14] This also allows for donors to be made aware of the tangible outcomes that have been achieved by the network. Interim goals that are achievable within a short span of time also afford a sense of legitimacy onto the network, allowing it to deliver on its mandate early on. Setting milestones would require an in depth focus and a nuanced understanding of specific aspects of larger problems and delivering early results on these problems would allow for a foundational base of trust, on the foundation of which, a possibly long drawn out consultative process can be fixed.[15]

A Network might often find alliances outside of its sector of operation. For example, Greenpeace was able to make its voice heard in International Climate Change negotiations by engaging with private insurance companies and enlisting their support.[16] The organization looked towards the private sector for support to mobilize resources and enlist the requisite expertise within their various projects. [17]

A. Funding

The financial support a network receives is essential for its sustenance. The initial seed money it receives can be obtained from a single source however, cross sectoral financing is necessary to build a consensus with regards to issues that may be a part of the network's mandate. The World Commission for Dams (WCD), for example, obtains funding from multiple sources in order to retain its credibility. The sources of funding of the WCD include government agencies, multilateral organizations, business associations, NGO's and Government Agencies, without a single donor contributing more than 10% of the total funding it receives.[18] However, the difficulty with this model of funding is the relative complexity in assimilating a number of smaller contributions, which may take away from its capacity to expand its reach and enhance the scope of its work. Cross sectoral funding is less of a fundamental requirement for networks whose primary mandate is implementation, such as The Global Environment Facility (GEF), whose legitimacy is derived from intergovernmental treaties and is therefore only funded by governments.[19] The GEF has only recently broadened its sources of funding to include external contributions from the private sector.

A network can also be funded through the objective it seeks to achieve through the course of its activities. For example, Rugmark an international initiative which seeks to mitigate the use of child labor in South Asia uses an external on site monitoring system to verify and provide labels certifying the production of carpets without the use of child labor.[20] The monitors of this system are trained by Rugmark and carpet producers have to sign a binding agreement, undertaking not to employ children below the age of 14 in order to receive the certification. The funds generated from these carpets, for the import of which American and European importers pay 1% of the import value, are used to provide rehabilitation and education facilities for the children in affected areas. The use of these funds is reported regularly. [21]

The funding must be sustained for a few years, which is a difficult task for networks that require an overall consensus of participants. The greatest outcomes of the network are not tangible solutions to the problem but the facilitation of an environment which allows stakeholders to derive a tangible solution. Thus, the elements of trust, communication and collaboration are integral to the efficient functioning of the network. However, the lack of tangible outcomes exposes the funders to financial risks. The best way to reduce such risks is to institute an uncompromising time limit for the initiative, within which it must achieve tangible results or solutions that can be implemented. A less stringent approach would be to incorporate a system of periodic review and assessment of the accomplishments of the network, subsequent to which further recommendations may be made for a further course of action.[22]

B. Relationships

A three year study conducted by Newell & Swan drew definitive conclusions with respect to the inter-organizational collaboration between participants within a network. The study determined that there currently exist three types of trust; Companion trust or the trust that exists within the goodwill and friendship between participants, Competence trust, wherein the competence of other participants to carry out the tasks assigned to them is agreed upon and lastly, Commitment trust or the trust which is predicated on contractual or inter-institutional that are agreed upon. [23] While companion and competence trust are easily identifiable, commitment trust is more subjective as it is determined by the agreement surrounding the core values and overall identifiable aims. Sheppard & Tuchinsky refer to an identification based trust which is based on a collective understanding of shared values. Such a trust requires significant investment but they argue, "The rewards are commensurably greater and the he benefits go beyond quantity, efficiency and flexibility." [24] Powell postulates, "Trust and other forms of social capital are moral resources that operate in fundamentally different manner than physical capital. The supply of trust increases, rather than decreases, with use: indeed, trust can be depleted if not used." [25]

Karl Wieck endorses the "maintenance of tight control values and beliefs which allow for local adaptation within centralized systems." [26] The autonomy that participants within a network enjoy is therefore considered to be close to sacred, so as to allow them to engage with each other on an equitable footing, while still maintain their individual identities. Freedman and Reynders believe that networks place a so called 'premium' on " the autonomy of those linked through the network…..networks provide a structure through which different groups - each with their own organizational styles, substantive priorities, and political strategies - can join together for common purposes that fill needs felt by each. "[27] Consequently, lower the level of centralized control within a network, the greater the requirement of trust. Allen Nan resonates with this idea, as is evident from her review of coordinating conflict resolution NGO's. She believes that these NGO's are most effective when " beginning with a loose voluntary association which grows through relationship building, gradually building more structure and authority as it develops. No NGO wants to give away its authority until it trusts a networking body of people that it knows. " [28]

C. Communication and Collaboration

The binding force that ties together any network is the importance of relationships between participants and their interactions with organizations outside the network. Research has shown that face to face interaction works best and although email may be practical, a face-to-face meeting at regular intervals builds a level of trust amongst participants. [29] It is however important to prevent network from turning into 'self-selecting oligarchies' and to prevent this, there needs to be a balance drawn between goodwill and the trust in others' competence along with a common understanding of differently hierarchized values. [30]

There is also an impending need to develop a relationship vocabulary, as suggested by Taylor, which would be of particular use within transnational networks and afford a deeper understanding of cross cultural relationships.[31]

D. Participation

A significant issue that networks today have to address is how to inculcate and then subsequently maintain participation in the activities of the network. This would include providing incentives to participants, encouraging diversity and enabling greater creative inflow across sectors to generate innovative output. Participation involves three fundamental elements; Action, which includes active contribution in the form of talking, listening, commenting, responding and sharing information, Process, which aids in an equitable system of decision making and constructing relationships and the underpinned values associated with these two elements, which include spreading equality, inculcating openness and including previously excluded communities or individuals. [32] Participation in itself envisages a three leveled definition; participation as a contribution, where people offer a tangible input, participation as an organization process, where people organize themselves to influence certain pre-existing processes and participation as a form of empowerment where people seek to gain power and authority from participating.

In order to create an autonomous system of evaluating and monitoring the nature and context of participation, a network would have to attempt to systematically incorporate a few fundamental processes, such as; enabling an understanding of the dynamism of a network through an established criteria of monitoring the levels of participation of the members, creating an explicit checklist of qualifications of this participation, such as the contributions of the participants, the limits of commitment and the available resources that must be shared and distributed, acknowledging the importance of relationships as fundamental to the success of any network., building a capacity for facilitative and shared leadership, tracing the changes that occur when the advocacy and lobbying activities of individuals are linked and using these individuals as participants who have the power to influence policy and development at various levels.[33] Finally, the recognition that utilizing the combined faculties of the network would aid in the effectuation of further change is vital to sustaining an active participation in the network.[34] It is common for networks to stagnate simply because of the lack of clarity on what a network really is or what it entails. There are significant misconceptions as to the activities engaged in by the network, such as the idea that a network "works solely as a resource center, to provide information, material and papers, rather than as forums for two way exchanges of information and experiences," contribute to the misunderstanding regarding the participation requirements within a network.[35] To facilitate an active, participatory function of learning, a network needs to be more than a resource center that seeks to meet the needs of beneficiaries. While meeting these needs is essential, development projects tend to obfuscate the benefit/input relationship within a network, thus significantly depleting its dynamism quotient. [36]

One method of moving away from the needs based model is to create a tripartite functionary, as was created within a particular research study. [37] This involves A Contributions Assessment, A Weaver's Triangle for Networks and An identification of channels of participation.

Contributions Assessment is an analysis of what the participants within a network are willing to contribute. It enables the network to assess what resources it has access to and how those resources may be distributes amongst the participants, multiplied or exchanged. [38] This system is predicated on a premise of assessing what participants have to offer as opposed to what they need. It challenges the long held notion of requiring an evaluation to identify problems, to address which recommendations are made and in fact seeks to focus on the moments of excellence and enable a discussion on the factors that contributed to these moments. [39] It thus places a value on the best of "what is" as opposed to trying to find a plausible "what ought to be". This approach allows participants to recognize that they are in fact the real "resource Centre" of the network and are encouraged act accordingly.

A Contributions Assessment may be practically incorporated through a few steps. It must be focused on the contributions, after a discussion on who the contributors may be. The aims of the network must be clarified, along with a specification of the contributions required such as perhaps newsletters, a conference, policy analysis etc. The members of the network must be clear on what they would like to contribute to the network and how such contribution might be delivered. Finally, the secretariat must be able to ideate or innovate on how it can enable more contributions from the networks in a more effective manner. [40]

The Weaver's Triangle has been adapted to be applies within networks and enables participants to understand what the aims and activities of the network are. It identifies the overall aim of the network and the change the network seeks to bring about to the status quo. It then lays out the objectives of the network in the form of specific statements about the said differences that the network seeks to bring about. Finally, the network would have to explain why a particular activity has been chosen. [41] The base of the triangle reflects the specific activities that the network seeks to engage in to achieve the said objectives. The triangle is further divided into two, to ensure that action aims and process aims have equal weightage; this allows for the facilitation of an exchange and a connection between the members of the network. [42]

The Circles of Participation is an idea that has been put forth by the Latin American and Caribbean Women's Health Network. (LACWHN). [43] This Network has three differentiated categories of membership, which it uses to determine the degree of commitment of an organization to the network. R- refers to the members who receive the women's health journal, P refers to members who actively participate in events and campaigns and who are advisors for specific topics. PP refers to the permanent participants within the network at national and international levels. They also receive a journal. This categorization allows the network to make an assessment of the dynamism and growth of a network, with members moving through the categories depending on their levels of participation. [44]

An important space for contributions to the network is the newsletter. This can be facilitated by allowing contributions from various sources, provided they meet the established quality checks, ensuring a balance between regions of origin of the members of the network, ensuring a balance between the policy and program activities of the members and keeping the centralized editorial process to a minimum. This is in keeping with the ideal of a decentralized system of expression that allows each member to retain its individuality while still contributing to the aims of the network. The Women's Global Network on Reproductive Rights (WGNRR) sought to create a similar system of publication to measure the success of their linkages, the levels of empowerment amongst members, in terms of strategizing and enabling localized action and the allocation of space in a fair and equitable manner. [45] Another Network, Creative Exchange customizes its information flow within the network so that each member only receives the information it expresses interest in.[46] This prevents the overburdening of members with unnecessary information.

The activities of the network which don't directly pass through the secretariat or the coordinator of the network can be monitored efficiently by keeping I close contact with new entrants to the network and capturing the essence of the activities that occur on the fringes of the network. This would allow an assessment of the diversity of the network. For example, Creative exchange sends out short follow up emails to determine the number and nature of contacts that have been made subsequent to a particular item in the newsletter. The UK Conflict Development and Peace Network (CODEP) records the newest subscribers to the network after every issue of their newsletter and AB Colombia sends out weekly news summaries electronically which are available for free to recipients who provide details of their professional engagements and why or how they wish to use these summaries. [47] This enables the mapping of the type of recipients the information reaches.

E. Leadership and Coordination

Sarason and Lorentz postulate four distinguishing characteristics that capture the creativity and expertise required by individuals leading and coordinating networks.[48] Knowledge of the territory or a broad understanding of the type of members, the resources available and the needs of the members is extremely important to facilitate an ideal environment of mutual trust and open dialogue between the members. Scanning the network for fluidity and assessing openings, making connections and innovating solutions would enable an efficient leadership that would contribute to the overall dynamism of the network. In addition to this, perceiving strengths and building on assets of existing resources would allow the network to capitalize on its strengths. Finally, the coordinators of a network must be a resource to all members of the network and thus enable them to create better and more efficient systems. They must therefore exercise their personal influence over members wherever required for the overall benefit of the network. Practically, a beneficial leadership would also require an inventive approach by providing fresh and interesting solutions to immediate problems. A sense of clarity, transparency and accountability would also encourage members of the network to participate more and engage with each other. It is important for the leadership within a network to deliver on expectations, while building consensus amongst its members.

A shared objective, a collaborative setting and a constant review of strategies is important to maintain linkages within a network. Responsible relationships underpinned by values and supported by flows of relevant information would allow an effective and fruitful analysis by those who are engaged within a network to do the relevant work. In addition to this, a respect for the autonomy of the network is essential.

F. Inclusion

Public policy networks are more often than not saturated with the economic and social elite from across the developed world. A network across the Global South would have to change this norm and extend its ambit of membership to grass root organizations, which might not have otherwise had the resources or the opportunity to be a part of a network.[49] Networks can achieve their long term goals only if they are driven by the willingness to include organizations from across economic demographics. This would ensure that their output is the result of a collaborative process that takes into account cross cultural norms and differentials across economic demographics.

The participation of diverse actors is reflective of the policy making processing having given due regard to on the ground realities and being sensitive towards the concerns of differently placed interest groups. Networks have been accused of catering only to the needs of industrial countries and subscribing to values of the global north thus stunting local development and enforcing double standards. This tarnishes the legitimacy of the processes inculcated within the network itself. It is therefore all the more essential that a network focused on the global south have a diverse collection of members from across backgrounds and economic contexts. Additionally, the accountability of the network to civil society is dependent on the nature of the links it maintains with the public. Inclusion thus fosters a sense of legitimacy and accountability. The inclusion of local institutions from the beginning would also increase the chances of the solutions provided by the network, being effectively implemented. Local inclusion affords a sense of responsibility and ensures that the network would remain sustainable in the long run. Allowing local stakeholders to take ownership of the network and participate in the formulation of policies, engage in planning and facilitate participation would enable an efficient addressing of significant public policy issues. [50] Thus networks would need to create avenues for participation of local institutions and civil society to engage in a democratic form of decision making.

III. Evaluation

The process of evaluation of a network is most efficiently effectuated through a checklist that has been formulated within a research study for the purpose of evaluating its own network. [51]

This checklist enumerates the various elements that have to be taken into consideration while evaluating the success of a network, as follows;

FIG 1.[52]

V. Learning and Recommendations

In order to facilitate the optimum working of a network several factors need to be taken into consideration and certain specific processes have to be incorporated into the regular functioning of the network. These are for example,

• Ensuring that the evaluation of the network occurs at periodic intervals with the requisite level of attention to detail and efficiency to enable an in depth recalibration of the functions and processes of the network. To this effect, evaluation specialists must be engaged not just at times of crises or instability but as accompaniments to the various processes undertaken by the network. This would enable a holistic development of the network.
• It is also important to understand the underlying values that define the unique nature of the network. The coordination of the network, its functions and its activities are intrinsically linked to these values and recognition of this element of the network would enable a greater functionality in the overall operation of the network.
• A strong relationship between the members of the network, predicated on trust and open dialogue is essential for its efficient functioning. This would allow the accumulation of innovative ideas and dynamic thought to direct the future activities of the network.
• The Secretariat or coordinator of the network must be able to engage the member in monitoring and evaluating the progress of the network. One method of enabling this coordination is through the institution of 'participant observer' methods at international conferences or meetings, which allow the members of the network to report back on the work that they have, which is linked to the work of other members.
• The autonomy of a network and its decentralized mechanism of functioning are integral to retain the individuality of its members, who seek to pursue institutional objectives. The members seek to facilitate creative thinking and share ideas and this must be supported by financial resources. A strong bond of trust between the members of a network is therefore essential to enable long term commitments and the flourishing of interpersonal communication between members.
• It is important that the subject area of operation of the network be comprehensively defined before the network comes into existence.
• As seen with the experience of Canadian Knowledge Networks, it is beneficial to be selective in inviting participant to the network and following a rigorous process of review and selection would ensure that only the best candidates are selected so as to facilitate effective partnerships with other networks, as a result of demonstrable expertise within a particular field.
• The management of a network must be disciplined, with clearly demarcated project deadlines and an optimum level of transparency and accountability. At the helm of leadership of every successful network, there has been intelligent, decisive and facilitative exchange, which is essential in securing a durable and potentially expandable space for the network to operate in.

A. Canadian Perspectives

A study of Canadian experiences was conducted by examining The Centers of Excellence and the Networks of Centers of Excellence (NCEs), which were funded through three Federal Granting Councils.[53] An initial observation that was made through the course of this study was that each network is intrinsically different and there is no uniform description which would fit all of them. The objectives of the Networks of Centers of Excellence Program are broadly, as follows; to encourage fundamental and applied research in fields which are critical to the economic development of Canada, to encourage the development and retention of world class scientists and engineers specializing in essential technologies, to manage multidisciplinary, cross sectoral national research programs which integrate stakeholder priorities through established partnerships and finally, to accelerate the exchange of research results within networks by accelerating technology transfers, made to users for social and economic development. [54] Extensive interviews carried out in the course of the research conducted by the ARA Consulting Group Inc. drew up particularly relevant conclusions with respect to the NCEs.

Firstly, they have been able to produce significant "cultural shifts" among the researchers associated with the network. This is attributed to the network facilitating a collaborative effort amongst researchers as opposed to their previous working, which was largely in isolation. The benefits of this collaboration have been identified as providing innovative ideas and leading the research itself in unprecedented directions. This has the effect of equipping Canada with the capability to compete on a global level with respect to its research endeavors. The culture shift has also allowed researchers to be more aware of the problems that plague industry and has instigated more in depth research into the development of the industrial sector. Government initiatives that have attempted to cohesively apply academic research to industry have had limited success. The NCE's however have managed to successfully disintegrate the barriers between these two seemingly disparate fields. This has resulted in a faster and more effective system of knowledge dissemination resulting in durable and self-sustaining economic development, which takes place at a faster rate. The NCE's have also been able to contribute to healthcare, wellness and overall sustainable development through their cross sectoral research approach, a model that can be used worldwide.

Another tangible effect has been that the relationship between industry and academic research is evolving into a positive and collaborative exchange, as opposed to the previous state which was largely isolationist, bordering on confrontational.[55] A possible cause of this is the increased representation of companies in the establishment of networks resulting in them influencing the course of research. This has not been met with any resistance from academic researchers who are driven by the imperative of an open publication. [56] Besides influencing the style of management, industrial representation has also brought about an increase in the level of private sector financial contributions made to NCEs. It is believed that these NCEs may even be able to support themselves in the next 7-8 years through the funding they receive from the commercialization of their research.

A third benefit that has emerged is the faster rate of production of new knowledge and innovative thinking. This is the result of collaborative techniques which is made more efficient through the use of modern technology. The increasing number of multi authored cross institutional scholarly publications made available by the NCE is evidentiary of this trend. The rate and quantity of technology transfers has also increased exponentially as a result of this. Knowledge networks also facilitate the mobilization of human resources and address cross disciplinary problems, resulting in an efficient and synergistic solutions. Their low cost, fast pace approach has been instrumental in constructing an understanding of and capacity to engage in sustainable development.

The significant contributions to sustainable development include the Canadian Genetic Diseases Network, which has discovered two specific genes that cause early onset Alzheimer's disease. The Sustainable Forest Management Network has claimed that its research does have a considerable level of influence on the industrial approach to sustainability. The Canadian Bacterial Disease Network conducts research on bacterially caused diseases which are mostly prevalent in developing countries, with a view to produce antibiotics and vaccines that may be able to successfully combat these vaccines. TeleLearning, another such network is working on the creation of software environments which will form the basis of technology based education in the future. [57] The greatest advantage of these knowledge networks is that they have been able to surpass traditional disciplinary barriers and have emerged at the forefront of interdisciplinary articulation, which is emerging as the path to breakthroughs in the fields of applied sciences and technology in the future. The NCE's have also been able to provide diverse working environments for graduate students, where they have been able to work under scientists associated with different specializations and across different departments. They have also been able to interact with government and industry representatives, giving them a far greater exposure of the field and equipping them to avail of a wide range of employment opportunities.

The corporate style of management incorporated within the NCEs encourages a sense of discipline and an enthusiasm for innovation. The Board of Directors at NCE's take on a perfunctory role and function as a typical corporate board. Researchers are therefore required to provide regular reports and meet deadlines to achieve predetermined goals that have been agreed upon. The new paradigm of sustainable development and the fluid transfer of knowledge requires this structure of management, even within a previously strictly academically oriented environment. NCEs have been incorporated as non-profit corporation for largely legal reasons such as the ownership of intellectual property.

The participation to these networks is restricted and is open only through an invitation, in the form of a submission of project proposals under a particular theme, with the final selection being made subject to a rigorous process of evaluation. This encourages the participants of the network to embody a degree of discipline and carry out their activities in a constructive, time bound manner.

B. Perceived Challenges

These knowledge networks, although extremely beneficial in the long run, do have certain specific issues that need to be addressed. Firstly, most formal knowledge networks do not have a formalized communication strategy. While they do make use of various forms of telecommunication, this communication is is no way formally directed or specific. Although some networks have managed to set up a directed communications strategy, supplemented by the involvement of specifically communications based networks (such as CANARIE) , there is still a long way to go in this area.

As is evident with most academic endeavors in recent years, efficient and sustained development both in terms of economy as well as self-sustenance, requires a smooth transitioning to a close collaboration with the industry. Although the NCE's have made progress in this area, a lesson that can be learnt from this is that knowledge networks do require a collaborative arrangement between researchers, the industry and the financial sector. [58] The nature of this collaboration cannot be predicted before tangible research outputs are developed that reflect the relevance of academia in the industrial and financial sectors. A particular network, PENCE has mandated that the boards of directors include a representative of the financial sector. This is a step forward in opening the doors to greater collaboration and mutually assured growth and sustainable development in both academia as well as the industrial and financial sectors.

As with all knowledge networks there is a continuous need for expansion of the focus areas to cover more fields and instigate research in neglected areas. The largest number of networks has been in the fields of healthcare and health associated work. However there is an impending need for networks to be established in other fields as well, such as those related to environmental issues, social dynamics and the general quality of life. [59]

The Canadian experience has resulted in a nuanced understanding of specific actions that need to be taken to strengthen knowledge networks across the spectrum. Firstly, there is an impending need to build new knowledge networks, which would be required to strengthen institutions upon which the networks are based. These include universities and research institutions, which have been weakened both financially and academically over the past few years. The NCE Program, on the face of it, seems to be strengthening universities, by attracting funding for research endeavors that would otherwise not be available to them. While this may be true, it tends to obfuscate the true nature of a university as an intellectual community, by portraying it as a funding source for research and equipment.[60] The deteriorating role of the university in fostering research and laying the foundation of an intellectual community can be reversed by the competition posed by the NCEs which tend to threaten its stature in the fields of multi-disciplinary and graduate institution. Another aspect that needs to be considered is the role of knowledge networks in fostering sustainable development not only on a national or regional scale but on a global level. This can be effectuated by allowing the amalgamation of the academia and industry through ample representation, a model that has proven to be effective within the NCEs. This is all the more relevant today where multinational corporations hold considerable sway over the global economy, so much so that the role of governments in regulating this economy is gradually decreasing. Multilateral investment treaties and agreements are reflective of this.

The final issue is that of the long standing debate between public good and proprietary knowledge. Canadian knowledge networks are of the opinion that knowledge must be freely disseminated. However, certain networks including the NCEs grant the exclusive right of the development and application of this knowledge to specific industry affiliates. On one hand this facilitates further investment into the research, which creates better products, new jobs and further social development. This is predicated on a fine balance of allowing this development without widening the already disparate socio-economic gaps that exist between developed and developing countries. Thus the balance between public good and propriety knowledge must be effectively managed by the regulatory role discharged by the governments and the decision making faculties of these knowledge networks. [61]

Establishing international linkages across networks based within different regions across the world would also be an effective means of ensuring effective partnerships and the creation of a new, self-sustaining structure. This would bring new prospects of funding into sustainable development activities and engage industrial affiliates with international development activities.

C. Donor Perspectives

The International Development Research Centre, based in Canada has also been instrumental in the setting up of support structures for networks. The IDRC has remained consistent in its emphasis of networks as mechanisms of linking scientists engaged in similar problems across the globe instead of as mechanisms to fund research in countries. This has afforded the IDRC with a greater level of flexibility in responding to the needs of developing countries as well as responding to the financial pressures within Canada to deliver superior technical support with a reduction in overheads. The IDRC sees networking an indispensable aspect of scientific pursuit and technological adaptation in the most effective manner. It is currently supporting four specific types of networks; horizontal networks which link together institutions with similar areas of specialization, vertical networks which work on disparate aspects of the same problem of different but interrelated problems, information networks which provide a centralized form of information service to members, which enables them to exchange information in the manner necessary and finally training networks which provide supervisory services to independent participants within the network.[62]

(I) Internal Evaluations

There is an outstanding need to monitor visits that are undertaken by the coordinator or the specific representatives of the member or donor as applicable. This would expedite the process of identifying problems and aid in deriving tangible solutions in an efficient manner. The criteria for the assessment would vary depending on the goals of the organization. Donors may pose questions with respect to the cost effectiveness of a particular pattern of research and may seek a formal report regarding this aspect. A more extensive model of donor evaluations may even include assessments with respect to the monitoring and coordination of specific functions.

(II) External Evaluations

A system of external evaluation would be useful with assessing data with respect to the operations of programs and their objectives. This would engage newer participants by injecting newer ideas and insights into the management and scope of the network. The most extensive method of network evaluation was one that was postulated by Valverde [63] and reviewed by Faris [64]. It aimed to draw an analysis of particular constraints and specific elements that would influence the execution of network programs. This method identifies a list of threats, opportunities, strengths and weaknesses which would inform future recommendations. The Valverde method makes use of both formal as well as informal data which is varied depending on the type of network and the management structure it employs.[65]

(III) Financial Viability

A network almost always requires external resources to aid in the setting up and coordination of its activities. Donor agencies must recognize the long term commitment that is required in this respect. It is therefore essential that the period for which this funding will be made available be clarified at the outset, to leave agencies with ample time to plan for the possibility of cessation of external financial support. [66] As concluded from the findings of the research study, although most networks are offered external support, it is primarily technology transfer and information networks that have been able to generate the bulk of funding in this respect. They have been able to obtain this financial assistance from a variety of sources including participating organizations as well as governments. [67] The funding for purely research networks however are inconsistent and the networks would have to plan in advance for a possible cessation of financial support.[68]

(IV) Adaptability

From the perspective of donors, the degree of adaptability and level of responsiveness of a particular network is especially relevant in assessing the coordination, control and leadership of a particular network. A network that is plagued by ineffective leadership and the lack of coordination is unable to adapt to changing circumstances and meet the needs of its participants. A combination of collaborative effort, a localized approach and far-sighted leadership instills in the participants of the network a sense of comfort in its processes and in the donors a faith in its ability to address topical issues and remain relevant.

(V) The Exchange of Information

As noted by Akhtar, a network is created to respond to the growing need to improve channels of information exchange and communication. [69] Information needs to be tailored to suit its users and must be disseminated accordingly. The study conducted has concluded that information networks that are engaged in the transfer of technology are inefficient in disseminating internally derived information and recognizing the needs of their users.[70] Given that these networks are especially user oriented this systemic failure is extremely problematic. There is also a need to review the mechanism of transferring strategic research techniques and the approaches employed in dealing with developing countries. Special attention must be paid to the beneficiaries of a particular network so that the research conducted is directed towards that particular demographic. This is especially relevant for information networks, which from the evaluation; appear to be generating data but not considering who would be using these services.[71]

(VI) Capacity Building

Facilitating the training of individuals both on a formal and informal level has led to an enhance level of research and reporting, as well as the designing of projects. There is however a need to tailor this training to suit the needs of the participants of a particular network. Networks which have been able to provide inputs which are not ordinarily locally provided have instigated the establishment of national and regional institutions. [72]

(VII) Cost Effectiveness

It is important to note however that networks need to employ the most cost effective mechanism of delivering support services to national programs. A network must work in a manner that allows for enough individual enterprise but at the same time follows a collaborative model to generate more effective and relevant research within a short span of time and through the utilization of minimum resources. The Caribbean Technology Consultation Services (CTCS) for example was found to be far more cost effective and in fact 50% cheaper than the services of the United Nations Industrial Development Organization. [73] Similarly, the evaluators of the LAAN found that funding a network was significantly cheaper than finding individual research projects.[74]

Introduction

In 2008 Chris Anderson infamously proclaimed the 'end of theory'. Writing for Wired Magazine, Anderson predicted that the coming age of Big Data would create a 'deluge of data' so large that the scientific methods of hypothesis, sampling and testing would be rendered 'obsolete' [1]. For him and others, the hidden patterns and correlations revealed through Big Data analytics enable us to produce objective and actionable knowledge about complex phenomena not previously possible using traditional methodologies. As Anderson himself put it, 'there is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot' [2] .

In spite of harsh criticism of Anderson's article from across the academy, his uniquely (dis)utopian vision of the scientific utility of Big Data has since become increasingly mainstream with regular interventions from politicians and business leaders evangelising about Big Data's potentially revolutionary applications. Nowhere is this bout of data-philia more apparent than in India where the governments recently announced the launch of 'Digital India', a multi-million dollar project which aims to harness the power of public data to increase the efficiency and accessibility of public services [3]. In spite of the ambitious promises associated with Big Data however, many theorists remain sceptical about its practical benefits and express concern about its potential implications for conventional scientific epistemologies. For them the increased prominence of Big Data analytics in science does not signal a paradigmatic transition to a more enlightened data-driven age, but a hollowing out of the scientific method and an abandonment of casual knowledge in favour of shallow correlative analysis. In response, they emphasise the continued importance of theory and specialist knowledge to science, and warn against what they see as the uncritical adoption of Big Data in public policy-making [4]. In this article I will examine the challenges posed by Big Data technologies to established scientific epistemologies as well as the possible implications of these changes for public-policymaking. Beginning with an exploration of some of the ways in which Big Data is changing our understanding of scientific research and knowledge, I will argue that claims that Big Data represents a new paradigm of scientific inquiry are predicated upon a number of implicit assumptions about the nature of knowledge. Through a critic of these assumptions I will highlights some of the potential risks that an over-reliance on Big Data analytics poses for public policy-making, before finally making the case for a more nuanced approach to Big Data, which emphasises the continued importance of theory to scientific research.

Big Data: The Fourth Paradigm?

"Revolutions in science have often been preceded by revolutions in measurement".

In his book the Structure of Scientific Revolutions Kuhn describes scientific paradigms as 'universally recognized scientific achievements that, for a time, provide model problems and solutions for a community of researchers'[5]. Paradigms as such designate a field of intelligibility within a given discipline, defining what kinds of empirical phenomena are to be observed and scrutinized, the types of questions which can be asked of those phenomena, how those questions are to be structured as well as the theoretical frameworks within which the results can be analysed and interpreted. In short, they 'constitute an accepted way of interrogating the world and synthesizing knowledge common to a substantial proportion of researchers in a discipline at any one moment in time'[6]. Periodically however, Kuhn argues, that these paradigms can become destabilised by the development of new theories or the discovery of anomalies that cannot be explained through reference to the dominate paradigm. In such instances Kuhn claims, the scientific discipline is thrown into a period of 'crisis', during which new ideas and theories are proposed and tested, until a new paradigm is established and gains acceptance from the community.

More recently computer scientists Jim Gray, adopted and developed Kuhn's concept of the paradigm shift, charting history of science through the evolution of four broad paradigms, experimental science, theoretical science, computational science and exploratory science [7]. Unlike Kuhn however, who proposed that paradigm shifts occur as the result of anomalous empirical observations which scientists are unable to account for within the existing paradigm, Gray suggested that transitions in scientific practice are in fact primarily driven by advances and innovations in methods of data collection and analysis. The emergence of the experimental paradigm according to Gray can therefore be traced back to the ancient Greece and China when philosophers began to describe their empirical observations using natural rather an spiritual explanations. Likewise, the transition to the theoretical paradigm of science can be located in the 17^th Century during which time scientists began to build theories and models which made generalizations based upon their empirical observations. Thirdly, Gray identifies the emergence of a computational paradigm in the latter part of the 20^th Century in which advanced techniques of simulation and computational modelling were developed to help solve equations and explore fields of inquiry such as climate modelling which would have been impossible using experimental or theoretical methods. Finally, Gray proposed that we are today witnessing a transition to a 'fourth paradigm of science', which he termed the exploratory paradigm. Although also utilising advanced computational methods, unlike the previous computational paradigm which developed programs based upon established rules and theories, Gray suggested that within this new paradigm, scientists begin with the data itself; designing programs to mine enormous databases in the search for correlations and patterns; in effect using the data to discover the rules [8].

The implications of this shift are potentially significant for the nature of knowledge production, and are already beginning to be seen across a wide range of sectors. In the retail sector for example, data mining and algorithmic analysis are already being used to help predict items that a customers may wish to purchase based upon previous shopping habits[9]. Here, unlike with traditional research methodologies the analysis does not presuppose or hypothesise a relationship between items which it then attempts to prove through a process of experimentation, instead the relationships are identified inductively through the processing and reprocessing of vast quantities of data alone. By starting with the data itself, Big Data analysts circumvent the need for predictions or hypothesis about what one is likely to find, as Dyche observes 'mining Big Data reveals relationships and patterns that we didn't even know to look for'[10]. Similarly, by focussing primarily on the search for correlations and patterns as opposed to causation Big Data analysts also reject the need interpretive theory to frame the results instead researchers claim the outcomes are inherently meaningful and interpretable by anyone without the need for domain specific or contextual knowledge. For example, Joh observes how Big Data is being used in policing and law enforcement to help make better decisions about the allocation of police resources. By looking for patterns in the crime data they are able to make accurate predictions about the localities and times in which crimes are most likely to occur and dispatch their officers accordingly[11]. Such analysis according to Big Data proponents requires no knowledge of the cause of the crime, nor the social or cultural context within which it is being perpetrated, instead predictions and assessments are made purely on the basis of patterns and correlations identified within the historical data by statistical modelling.

In summary then, Gray's exploratory paradigm represents a radical inversion of the deductive scientific method, allowing researchers to derive insights directly from the data itself without the use of hypothesis or theory. Thus it is claimed, by enabling the collection and analysis of datasets of unprecedented scale and variety Big Data allows analysts to 'let the data speak for itself'[12], providing exhaustive coverage of social phenomena, and revealing correlations that are inherently meaningful and interpretable by anyone without the need for specialised subject knowledge or theoretical frameworks.

For Gray and others this new paradigm is made possible only by the recent exponential increase in the generation and collection of data as well as the emergence of new forms of data science, known collectively as "Big Data". For them the 'deluge of data' produced by the increase in the number of internet enabled devices as well as the nascent development of the internet of things, presents scientists and researchers with unprecedented opportunities to utilise data in new and innovative way to develop new insights across a wide range of sectors, many of which would have been unimaginable even 10 years ago. Furthermore, advances in computational and statistical methods as well as innovations in data visualization and methods of linking datasets, mean that scientist can now utilise the data available to its full potential or as professor Gary King quipped ' Big Data is nothing compared to a big algorithm'[13].

These developments in statistical and computational analysis combined with the velocity variety and quantity of data available to analysts have therefore allowed scientists to pursue new types of research, generating new forms of knowledge and facilitating a radical shift in how we think about "science" itself. As Boyd and Crawford note, ' Big Data [creates] a profound change at the levels of epistemology and ethics. Big Data reframes key questions about the constitution of knowledge, the processes of research, how we should engage with information, and the nature and the categorization of reality . . . [and] stakes out new terrains of objects, methods of knowing, and definitions of social life '[14]. For many these changes in the nature of knowledge production provide opportunities to improve decision-making, increase efficiency, encourage innovation across a broad range of sectors from healthcare and policing to transport to international development[15]. For others however, many of the claims of Big Data are premised upon some questionable methodological and epistemological assumptions, some of which threat to impoverish the scientific method and undermine scientific rigour [16].

Assumptions of Big Data

Given its bold claims the allure of Big Data in both the public and privates sectors is perhaps understandable. However despite the radical and rapid changes to research practice and methodology, there has nevertheless seemingly been a lack of reflexive and critical reflection concerning the epistemological implications of the research practices used in Big Data analytics. And yet implicit within this vision of the future of scientific inquiry lie a number of important and arguably problematic epistemological and ontological assumptions, most notably;

- Big Data can provide comprehensive coverage of phenomenon, capturing all relevant information.

- Big Data does not require hypothesis, a priori theory, or models to direct the data collection or research questions.

- Big Data analytics do not require theoretical framing in order to be interpretable. The data is inherently meaningful transcending domain specific knowledge and can be understood be anyone.

- Correlative knowledge is sufficient to make accurate predictions and guide policy decisions.

For many, these assumptions are highly problematic and call into question the claims that Big Data makes about itself. I will now look at each one in turn before proposing there possible implications for Big Data in Policy-making.

Firstly, whilst Big Data may appear to be exhaustive in its scope, it can only be considered to be so in the context of the particular ontological and methodological framework chosen by the researcher. No data set however large can scrutinize all information relevant to a given phenomenon. Indeed, even if it were somehow possible to capture all relevant quantifiable data within a specific domain, Big Data analytics would still be unable to fully account for the multifarious variables which are unquantifiable or undatafiable. As such Big Data does not provide an omnipresent 'gods-eye view', instead much like any other scientific sample it must be seen to provide the researcher with a singular and limited perspective from which he or she can observe a phenomenon and draw conclusions. It is important to recognise that this vantage point provides only one of many possible perspectives, and is shaped by the technologies and tools used to collect the data, as well as the ontological assumptions of the researchers. Furthermore, as with any other scientific sample, it is also subject to sampling bias and is dependent upon the researcher to make subjective judgements about which variables are relevant to the phenomena being studied and which can be safely ignored.

Secondly, claims by Big Data analysts to be able to generate insights directly from the data, signals a worrying divergence from deductive scientific methods which have been hegemonic within the natural sciences for centuries. For Big Data enthusiasts such as Prensky, 'scientists no longer have to make educated guesses, construct hypotheses and models, and test them with data-based experiments and examples. Instead, they can mine the complete set of data for patterns that reveal effects, producing scientific conclusions without further experimentation '[17]. Whereas, deductive reasoning begins with general statements or hypotheses and then proceeds to observe relevant data equipped with certain assumptions about what should be observed if the theory is to be proven valid; inductive reasoning conversely begins with empirical observations of specific examples from which it attempts to draw general conclusions. The more data collected the greater the probability that the general conclusions generated will be accurate, however regardless of the quantity of observations no amount of data can ever conclusively prove causality between two variables, since it is always possible that my conclusions may in future be falsified by an anomalous observation. For example, a researcher who had only ever observed the existence of white swans may reasonably draw the conclusion that 'all swans are white', whilst they would be justified in making such a claim, it would nevertheless be comprehensively disproven the day a black swan was discovered. This is what David Hume called the 'problem of induction'[18] and strikes at the foundation of Big Data claims to be able to provide explanatory and predictive analysis of complex phenomena, since any projections made are reliant upon the 'principle of uniformity of nature', that is the assumption that a sequence of events will always occur as it has in the past. As a result, although Big Data may be well suited to providing detailed descriptive accounts of social phenomena, without theoretical grounding it nevertheless remains unable to prove casual links between variables and therefore is limited in its ability to provide robust explanatory conclusions or give accurate predictions about future events.

Finally, just as Big Data enthusiasts claim that theory or hypotheses are not needed to guide data collection, so too they insist human interpretation or framing is no longer required for the processing and analysis of the data. Within this new paradigm therefore, 'the data speaks for itself' [19], and specialised knowledge is not needed to interpret the results which are now supposedly rendered comprehensible to anyone with even a rudimentary grasp of statistics. Furthermore, the results we are told are inherently meaningful, transcending culture, history or social context and providing pure objective facts uninhibited by philosophical or ideological commitments.

Initially inherited from the natural sciences, this radical form of empiricism thus presupposes the existence of an objective social reality occupied by static and immutable entities whose properties are directly determinable through empirical investigation. In this way, Big Data reduces the role of social science to the perfunctory calculation and analysis of the mechanical processes of pre-formed subjects, in much the same way as one might calculate the movement of the planets or the interaction of balls on a billiard table. Whilst proponents of Big Data claim that such an approach allows them to produce objective knowledge, by cleansing the data of any kind of philosophical or ideological commitment, it nevertheless has the effect of restricting both the scope and character of social scientific inquiry; projecting onto the field of social research meta-theoretical commitments that have long been implicit in the positivist method, whilst marginalising those projects which do not meet the required levels of scientificity or erudition.

This commitment to an empiricist epistemology and methodological monism is particularly problematic in the context of studies of human behaviour, where actions cannot be calculated and anticipated using quantifiable data alone. In such instances, a certain degree of qualitative analysis of social, historical and cultural variables may be required in order to make the data meaningful by embedding it within a broader body of knowledge. The abstract and intangible nature of these variables requires a great deal of expert knowledge and interpretive skill to comprehend. It is therefore vital that the knowledge of domain specific experts is properly utilized to help 'evaluate the inputs, guide the process, and evaluate the end products within the context of value and validity'[20].

Despite these criticisms however, Big Data is perhaps unsurprisingly increasingly becoming popular within the business community, lured by the promise of cheap and actionable scientific knowledge, capable of making their operations more efficient reducing overheads and producing better more competitive services. Perhaps most alarming from the perspective of Big Data's epistemological and methodological implications however, is the increasingly prominent role Big Data is playing in public policy-making. As I will now demonstrate, whilst Big Data can offer useful inputs into public policy-making processes, the methodological assumptions implicit within Big Data methodologies problems pose a number of risks to the effectiveness as well as the democratic legitimacy of public policy-making. Following an examination of these risks I will argue for a more reflexive and critical approach to Big Data in the public sector.

Big Data and Policy-Making: Opportunities and Risks

In recent year Big Data has begun to play an increasingly important role in public policy-making. Across the global, government funded projects designed to harvest and utilise vast quantities of public data are being developed to help improve the efficiency and performance of public services as well as better inform policy-making processes. At first glance, Big Data would appear to be the holy-grail for policy-makers - enabling truly evidence-based policy-making, based upon pure and objective facts, undistorted by political ideology or expedience. Furthermore, in an era of government debt and diminishing budgets, Big Data promises not only to produce more effective policy, but also to deliver on the seemingly impossible task of doing more with less, improving public services whilst simultaneously reducing expenditure.

In the Indian context, the government's recently announced 'Digital India' project promises to harness the power of public data to help modernise Indian's digital infrastructure and increase access to public services. The use of Big Data is seen as being central to the project's success, however, despite the commendable aspirations of Digital India, many commentators remain sceptical about the extent to which Big Data can truly deliver on its promises of better more efficient public services, whilst others have warned of the risk to public policy of an uncritical and hasty adoption of Big Data analytics [21]. Here I argue that the epistemological and methodological assumptions which are implicit within the discourse around Big Data threaten to undermine the goal of evidence based policy-making, and in the process widen already substantial digital divides.

It has long been recognised that science and politics are deeply entwined. For many social scientists the results of social research can be never entirely neutral, but are conditioned by the particular perspective of the researcher. As Shelia Jasanoff observed, 'Most thoughtful advisers have rejected the facile notion that giving scientific advice is simply a matter of speaking truth to power. It is well recognized that in thorny areas of public policy, where certain knowledge is difficult to come by, science advisers can offer at best educated guesses and reasoned judgments, not unvarnished truth' [22]. Nevertheless, 'unvarnished truth' is precisely what Big Data enthusiasts claim to be able to provide. For them the capacity of Big Data to derive results and insights directly from the data without any need for human framing, allows policy-makers to incorporate scientific knowledge directly into their decision-making processes without worrying about the 'philosophical baggage' usually associated with social scientific research.

However, in order to be meaningful, all data requires a certain level of interpretative framing. As such far from cleansing science of politics, Big Data simply acts to shift responsibility for the interpretation and contextualisation of results away from domain experts - who possess the requisite knowledge to make informed judgements regarding the significance of correlations - to bureaucrats and policy-makers, who are more susceptible to emphasise those results and correlations which support their own political agenda. Thus whilst the discourse around Big Data may promote the notion of evidence based policy-making, in reality the vast quantities of correlations generated by Big Data analytics act simply to broaden the range of 'evidence' from which politician can chose to support their arguments; giving new meaning to Mark Twain's witticism that there are 'lies, damn lies, and statistics'.

Similarly, for many an over-reliance on Big Data analytics for policy-making, risks leading to public policy which is blind to the unquantifiable and intangible. As already discussed above, Big Data's neglect of theory and contextual knowledge in favour of strict empiricism marginalises qualitative studies which emphasise the importance of traditional social scientific categories such as race, gender, and religion, in favour of a purely quantitative analysis of relational data. For many however consideration of issues such as gender, race, and religious sensitivity can be just as important to good public policy-making as quantitative data; helping to contextualise the insights revealed in the data and provide more explanatory accounts of social relations. They warn that neglect of such considerations as part of policy-making processes can have significant implications for the quality of the policies produced[23]. Firstly, although Big Data can provide unrivalled accounts of "what" people do, without a broader understanding of the social context in which they act, it fundamentally fails to deliver robust explanations of "why" people do it. This problem is especially acute in the case of public policy-making since without any indication of the motivations of individuals, policy-makers can have no basis upon which to intervene to incentivise more positive outcomes. Secondly, whilst Big Data analytics can help decision-makers to design more cost-effective policy, by for example ensuring better use of scarce resources; efficiency and cost-effectiveness are not the only metrics by which good policy can be judged. Public policy regardless of the sector must consider and balance a broad range of issues during the policy process including matters such as race, gender issues and community relations. Normative and qualitative considerations of this kind are not subject to a simplistic 1-0 quantification but instead require a great deal of contextual knowledge and insight to navigate successfully

Finally, to the extent that policy-makers are today attempting to harvest and utilise individual citizens personal data as direct inputs for the policy-making process, Big Data driven policy can in a very narrow sense be considered to offer a rudimentary form of direct democracy. At first glance this would appear to help to democratise political participation allowing public services to become automatically optimised to better meet the needs and preferences of citizens without the need for direct political participation. In societies such as India however, where there exist high levels of inequality in access to information and communication technologies, there remain large discrepancies in the quantities of data produced by individuals. In a Big Data world in which every byte of data is collected, analysed and interpreted in order to make important decisions about public services therefore, those who produce the greatest amounts of data, are better placed to have their voices heard the loudest, whilst those who lack access to the means to produce data risk becoming disenfranchised, as policy-making processes become configured to accommodate the needs and interests of a privilege minority. Similarly, using user generated data as the basis for policy decisions also leaves systems vulnerable to coercive manipulation. That is, once it has become apparent that a system has been automated on the basis of user inputs, groups or individuals may change their behaviour in order to achieve a certain outcome. Given these problems it is essential that in seeking to utilise new data resources for policy-making, we avoid an uncritical adoption of Big Data techniques, and instead as I argue below encourage a more balanced and nuanced approach to Big Data.

Data-Driven Science: A more Nuanced Approach?

Although an uncritical embrace of Big Data analytics is clearly problematic, it is not immediately obvious that a stubborn commitment to traditional knowledge-driven deductive methodologies would necessarily be preferable. Whilst deductive methods have formed the basis of scientific inquiry for centuries, the particular utility of this approach is largely derived from its ability to produce accurate and reliable results in situations where the quantities of data available are limited. In an era of ubiquitous data collection however, an unwillingness to embrace new methodologies and forms of analysis which maximise the potential value of the volumes of data available would seem unwise.

For Kitchen and others however, it is possible to reap the benefits of Big Data without comprising scientific rigour or the pursuit of casual explanations. Challenging the 'either or' propositions which favour either scientific modelling and hypothesis or data correlations, Kitchen instead proposes a hybrid approach which utilises the combined advantages of inductive, deductive and so-called 'abductive' reasoning, to develop theories and hypotheses directly from the data[24]. As Patrick W. Gross, commented 'In practice, the theory and the data reinforce each other. It's not a question of data correlations versus theory. The use of data for correlations allows one to test theories and refine them' [25].

Like the radical empiricism of Big Data, 'data-driven science' as Kitchen terms it, introduces an aspect of inductivism into the research design, seeking to develop hypotheses and insights 'born from the data' rather than 'born from theory'. Unlike the empiricist approach however, the identification of patterns and correlations is not considered the ultimate goal of the research process. Instead these correlations simply form the basis for new types of hypotheses generation, before more traditional deductive testing is used to assess the validity of the results. Put simply therefore, rather than interpreting data deluge as the 'end of theory', data-driven science instead attempts to harness its insights to develop new theories using alternative data-intensive methods of theory generation.

Furthermore unlike new empiricism, data is not collected indiscriminately from every available source in the hope that sheer size of the dataset will unveil some hidden pattern or insight. Instead, in keeping with more conventional scientific methods, various sampling techniques are utilised, 'underpinned by theoretical and practical knowledge and experience as to whether technologies and their configurations will capture or produce appropriate and useful research material'[26]. Similarly analysis of the data once collected does not take place within a theoretical vacuum, nor are all relationships deemed to be inherently meaningful; instead existing theoretical frameworks and domain specific knowledge are used to help contextualise and refine the results, identifying those patterns that can be dismissed as well as those that require closer attention.

Thus for many, data-driven science provides a more nuanced approach to Big Data allowing researchers to harness the power of new source of data, whilst also maintaining the pursuit of explanatory knowledge. In doing so, it can help to avoid the risks of uncritical adoption of Big Data analytics for policy-making providing new insights but also retaining the 'regulating force of philosophy'.

Conclusion

Since the publication of the Structure of Scientific Revolutions, Kuhn's notion of the paradigm has been widely criticised for producing a homogenous and overly smooth account of scientific progress, which ignores the clunky and often accidental nature of scientific discovery and innovation. Indeed the notion of the 'paradigm shift' is in many ways in typical of a self-indulgent and somewhat egotistical tendency amongst many historians and theorists to interpret events contemporaneous to themselves as in some way of great historical significance. Historians throughout the ages have always perceived themselves as living through periods of great upheaval and transition. In actual fact as has been noted by many, history and the history of science in particular rarely advances in a linear or predictable way, nor can progress when it does occur be so easily attributed to specific technological innovations or theoretical developments. As such we should remain very sceptical of the claims that Big Data represents a historic and paradigmatic shift in scientific practice. Such claims exhibit more than a hint of technological determinism and often ignore the substantial limitations to Big Data analytics. In contrast to these claims, it is important to note that technological advances alone do not drive scientific revolutions; the impact of Big Data will ultimately depend on how we decide to use it as well as the types of questions we ask of it.

Big Data holds the potential to augment and support existing scientific practices, creating new insights and helping to better inform public policy-making processes. However, contrary to the hyperbole surrounding its development, Big Data does not represent a sliver-bullet for intractable social problems and if adopted uncritically and without consideration of its consequences, Big Data risks not only to diminishing scientific knowledge but also jeopardising our privacy and creating new digital divides. It is critical therefore that we see through the hyperbole and headlines to reflect critically on the epistemological consequences of Big Data as well as its implications for policy making, a task unfortunately which in spite of the pace of technological change is only just beginning.

Bibliography

Anderson C (2008) The end of theory: The data deluge makes the scientific method obsolete. Wired, 23 June 2008. Available at: http://www.wired.com/science/discoveries/magazine/16-07/pb_theory (accessed 31 October 2015).

Bollier D (2010) The Promise and Peril of Big Data. The Aspen Institute. Available at: http://www.aspeninstitute.org/sites/default/files/content/docs/pubs/The_Promise_and_Peril_of_Big_Data.pdf (accessed 19 October 2015).

Bowker, G., (2013) The Theory-Data Thing, International Journal of Communication 8 (2043), 1795-1799

Boyd D and Crawford K (2012) Critical questions for big data. Information, Communication and Society 15(5): 662-679.

Cukier K (2010) Data, data everywhere. The Economist, 25 February (accessed 5 November 2015).

Department of Electronics and Information Technology (2015) Digital India, [ONLINE] Available at: http://www.digitalindia.gov.in/. [Accessed 13 December 15].

Dyche J (2012) Big data 'Eurekas!' don't just happen, Harvard Business Review Blog. 20 November. Available at: http://blogs.hbr.org/cs/2012/11/eureka_doesnt_just_ happen.html

Hey, T., Tansley, S., and Tolle, K (eds)., (2009) The Fourth Paradigm: Data-Intensive Scientific Discovery, Redmond: Microsoft Research, pp. xvii-xxxi.

Hilbert, M. Big Data for Development: From Information- to Knowledge Societies (2013). Available at SSRN: http://ssrn.com/abstract=2205145

Hume, D., (1748), Philosophical Essays Concerning Human Understanding (1 ed.). London: A. Millar.

Jasanoff, S., (2013) Watching the Watchers: Lessons from the Science of Science Advice, Guardian 8 April 2013, available at: http://www.theguardian.com/science/political-science/2013/apr/08/lessons-science-advice

Joh. E, 'Policing by Numbers: Big Data and the Fourth Amendment', Washington Law Review, Vol. 85: 35, (2014) https://digital.law.washington.edu/dspacelaw/bitstream/handle/1773.1/1319/89WLR0035.pdf?sequence=1;

Kitchen, R (2014) Big Data, new epistemologies and paradigm shifts, Big Data & Society, April-June 2014: 1-12

Kuhn T (1962) The Structure of Scientific Revolutions. Chicago: University of Chicago Press.

Mayer-Schonberger V and Cukier K (2013) Big Data: A Revolution that Will Change How We Live, Work and Think. London: John Murray

McCue, C., Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, Butterworth-Heinemann, (2014)

Morris, D. Big data could improve supply chain efficiency-if companies would let it, Fortune, August 5 2015, http://fortune.com/2015/08/05/big-data-supply-chain/

Prensky M (2009) H. sapiens digital: From digital immigrants and digital natives to digital wisdom. Innovate 5(3), Available at: http://www.innovateonline.info/index.php?view¼article&id¼705

Raghupathi, W., & Raghupathi, V. Big data analytics in healthcare: promise and potential. Health Information Science and Systems, (2014)

Shaw, J., (2014) Why Big Data is a Big Deal, Harvard Magazine March-April 2014, available at: http://harvardmagazine.com/2014/03/why-big-data-is-a-big-deal