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| Classification Space and Dynamic Evolution of Technological Weak Signals from Cognitive Perspective |
| Zhang Huiling1, Xu Haiyun2, Chen Liang4, Wang Chao2, Liu Chunjiang3, Wang Haiyan4 |
1.Taiyuan Library, Taiyuan 030024
2.School of Management, Shandong University of Technology, Zibo 255000
3.Chengdu Literature and Information Center, Chinese Academy of Sciences, Chengdu 610213
4.Institute of Scientific and Technical Information of China, Beijing 100038 |
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Abstract In this study, the cognitive characteristics and processes of weak signals in science and technology are systematically analyzed, a cognitive space for weak signals and a transformation framework model for multiple signal types oriented toward technology foresight are constructed, monitoring of evolutionary trends in weak signal themes is implemented, and an early identification of emerging frontier technologies is promoted. The connotations and characteristics of weak signals in science and technology are synthesized and analyzed. The cognitive characteristics of the identification of such weak signals are then decoded. By integrating cognitive and evolutionary perspectives, a signal cognitive space oriented toward technology foresight is constructed, enabling full-spectrum signal classification from a cognitive perspective. The characteristic differences and transformation paths among multiple weak-signal types are explored, and a“double-cone”model combining perception and cognition is developed for signal classification and transformation path analysis. A case study in the stem cell field validates the feasibility of the proposed signal classification space and evolutionary analysis model. Relying on the constructed classification space for weak technological signals, this model is used to analyze the transformation mechanisms and evolutionary paths between signals, capture weak signals in the innovation field, and utilize a classification system and multidimensional evolutionary analysis to achieve an early identification of weak signals and avoid signal loss, thereby enhancing the accuracy and recall rate of weak technological signal identification. It dynamically tracks state transitions and trends within the classification space, supporting the prediction of emerging frontier topics based on signal evolution patterns.
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Received: 21 March 2025
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