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Developing useful intelligence on scientific and technological emergence challenges those who would manage R&D portfolios, assess research programs, or manage innovation. Recently, the U.S. Intelligence Advanced Research Projects Activity Foresight and Understanding from Scientific Exposition Program has explored means to detect emergence via text analyses. We have been involved in positing conceptual bases for emergence, framing candidate indicators, and devising implementations. We now present a software script to generate a family of Emergence Indicators for a topic of interest. This paper offers some background, then discusses the development of this script through iterative rounds of testing, and then offers example findings. Results point to promising and actionable intelligence for R&D decision-makers. 

https://doi.org/10.1016/j.techfore.2018.04.016 Summary: Indicators of technological emergence promise valuable intelligence. We present an implemented algorithm to calculate emergence scores (EScores) for topical terms from abstract record sets. We offer a family of emergence indicators. Primary emergence indicators identify “frontier” terms based on their EScores. We then tally organizations, countries, or authors especially active in publishing (or patenting) on high EScore topics in a target R&D domain. We can score research fields on relative degree of emergence. This paper illustrates EScoring for Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data. 

The study of emerging technologies is broad and has multiple and often poorly integrated threads. For example, some literature draw from a number of characteristics such as radicalness, growth speed, coherence, impact, uncertainty and ambiguity while other only look at expected economic benefits. This fractured view of the growth of new technologies has created a hodgepodge of approaches and a dearth of fundamental measures within this research space. Recent efforts at developing a more fundamental measure of technological behavior have yielded "Technical Emergence" - a simple proposition which seeks to measure the growth of concepts within a community of users by tracking Novelty, Persistence, Community and Growth. This fundamental unit induces the possibility to actually measure and, more importantly test, its behavior using repeatable bibliometric techniques. We discuss in detail the conceptual origins and evaluate the concept of technological emergence and relations of indicators to it. 

https://doi.org/10.1080/09537325.2018.1480013 published online 

Indicators of technological emergence promise valuable intelligence to those determining R&D priorities. We present an implemented algorithm to calculate emergence scores for topical terms from abstract record sets. We offer a family of emergence indicators deriving from those scores. Primary emergence indicators identify “hot topic” terms, then we use those to generate secondary indicators that reflect organizations, countries, or authors especially active at research frontiers in a target domain. We also flag abstract records (papers or patents) rich in emergent technology content, and we score technological fields on relative degree of emergence. We show illustrative results for example topics -- Nano-Enabled Drug Delivery, Non-Linear Programming, Dye Sensitized Solar Cells, and Big Data.