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scientific collaborative networks can yield critical insights regarding the wider
geography of interactions among European cities.
The study of knowledge flows distinguishes two types of knowledge: codified
knowledge and tacit knowledge. Codified knowledge is defined as knowledge that
is stated in an explicit form. Explicit knowledge is relatively easily transferable
and can be transmitted through information channels and infrastructures. Tacit
knowledge is described by Michael Polanyi's famous quotation, “We can know
more than we can tell”. Hence, tacit knowledge is a non-linguistic and non-
numerical form of knowledge that essentially arises from personal experience and
skill. Tacit knowledge requires social interaction for transmission and is context
specific. Thus, it is difficult to capture, codify and transfer tacit knowledge.
Tacit knowledge is recognized as a central component of the “knowledge-based
economy.” As the key element for effective exploitation of innovative opportunities
and abilities, tacit knowledge crucially influences the innovation process. Therefore,
many studies focus on this form of knowledge. Generally, studies show that tacit
knowledge can be transmitted via collaboration between research centers, training
or personnel exchange.
From a geographical perspective, the mechanics of knowledge flows are unclear.
Studies commonly state that codified knowledge does not require proximity to be
shared and thus can flow easily across long distances. In contrast, tacit knowledge
requires proximity to be shared; thus, it is difficult to exchange tacit knowledge over
long distances. In reality, the geography of knowledge flows is far more complicated.
In addition to geographical proximity, additional types of proximity make it
possible to share tacit knowledge at long distances ( Comin , 2009 ). Here, we focus
on organizational proximity in analyzing scientific networks between European
cities created through collaborative Research and Development (R&D) projects
dedicated to converging technologies ( Boschma , 2005 ). Converging technologies
result from the merging of nanotechnology, biotechnology, information technology
and cognitive science (NBIC). Such technologies are expected to drive the future
innovation wave predicted to emerge by 2020 ( Nordmann , 2004 ).
The position of cities in scientific networks highlights the geographic structure
of European knowledge creation, which facilitates cities' economic competitiveness
in the “knowledge economy”. However, a large number of scientific exchanges with
other cities does not by itself make a city well positioned in a scientific network. The
criteria for judging cities' success also include their ability to ensure the intercon-
nection of national urban systems at the European scale ( Rozenblat , 1992 , 2004 ).
The remainder of this paper is divided into three sections. First, the empirical
data used to analyze European cities' position in scientific networks are presented.
Next, the structure of the European scientific network of cities is described and
compared to the structure of the European system of cities in terms of population
size. In urban geography, the population of a city is a good indicator of its position in
urban systems ( Pumain , 1997 ). Finally, particular attention is paid to the cities that
ensure the interconnection of national urban systems. These cities are essential in
expanding the spatial dimension of knowledge flows and linking the 27th European
national system of cities.
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