The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised-learning classification, and discuss the advantages and disadvantages of this approach vis-à-vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoningbased classification frameworks of scientific knowledge, as they typically try to fit new-to-the-world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large-scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large-scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.

Understanding the nature and dynamics of Africa's collaborative research networks is critical for building and integrating the African innovation system. This paper investigates the collaborative structure of the African research systems, with focus on regions and integration. Drawing on a bibliometric analysis of co-authorship of African research publications in 2005-2009, we propose an empirically derived grouping of African research community into three distinct research regions: Southern-Eastern, Western, and Northern. The three regions are established and defined in terms of active coauthorship clusters within Africa, as well as through co-authorship links with non-African countries and regions. We examine co-authorship links both at the national and city levels in order to provide a robust and nuanced empirical basis for the three African research regions. The collaboration patterns uncovered cast light on the emerging innovation systems in Africa by pointing out the differing national, regional, and global roles of countries and cities within collaborative research networks. Lack of research capabilities is the primary factor arresting the development of African innovation systems, but our analysis also suggests that Africa's internal research collaboration suffers from structural weaknesses and uneven integration. We also identify that South Africa, and some emerging new research hubs, hold critical networking function for linking African researchers.

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