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It is widely recognised that science in Africa will benefit from international research collaboration (IRC), and therefore studies have been done on IRC in Africa (hereafter: Africa-related IRC research). However, there is no information on the development of Africa-related IRC research, the geographical location of the scientists interested in the topic, the visibility of the literature and the themes researched. This information makes it possible to understand relevant aspects in the context of IRC in Africa, which are useful for identifying IRC strengths, weaknesses and opportunities. It also allows paving the way for future research on this topic. Using discipline–specific terms, bibliometric, and thematic analysis, I collected the literature on Africa-related IRC research indexed in the Web of Science Core Collection (WoS). The results showed that the number of publications on the topic has increased, few African countries have researched the topic, a third of the publications were written exclusively by African scientists, and the topic has high visibility. The panoply of publications revealed that patterns, driving factors, effects, networks, asymmetries, and policies concerning IRC were the main themes researched.
相似文献Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design.
相似文献