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1. Papers and patents are becoming less disruptive over time

   Park, Michael; Leahey, Erin; Funk, Russell J (January 2023, Nature)

   Theories of scientific and technological change view discovery and invention as endogenous processes1,2, wherein previous accumulated knowledge enables future progress by allowing researchers to, in Newton’s words, ‘stand on the shoulders of giants’3,4,5,6,7. Recent decades have witnessed exponential growth in the volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major advances8,9. Yet contrary to this view, studies suggest that progress is slowing in several major fields10,11. Here, we analyse these claims at scale across six decades, using data on 45 million papers and 3.9 million patents from six large-scale datasets, together with a new quantitative metric—the CD index12—that characterizes how papers and patents change networks of citations in science and technology. We find that papers and patents are increasingly less likely to break with the past in ways that push science and technology in new directions. This pattern holds universally across fields and is robust across multiple different citation- and text-based metrics1,13,14,15,16,17. Subsequently, we link this decline in disruptiveness to a narrowing in the use of previous knowledge, allowing us to reconcile the patterns we observe with the ‘shoulders of giants’ view. We find that the observed declines are unlikely to be driven by changes in the quality of published science, citation practices or field-specific factors. Overall, our results suggest that slowing rates of disruption may reflect a fundamental shift in the nature of science and technology. Data associated with this study are freely available in a public repository at https://doi.org/10.5281/zenodo.7258379. 

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2. Reliance on science: Worldwide front‐page patent citations to scientific articles

   https://doi.org/10.1002/smj.3145  

   Marx, Matt; Fuegi, Aaron (April 2020, Strategic Management Journal)

   Abstract <italic>Research summary</italic>To what extent do firms rely on basic science in their R&D efforts? Several scholars have sought to answer this and related questions, but progress has been impeded by the difficulty of matching unstructured references in patents to published papers. We introduce an open‐access dataset of references from the front pages of patents granted worldwide to scientific papers published since 1800. Each patent‐paper linkage is assigned a confidence score, which is characterized in a random sample by false negatives versus false positives. All matches are available for download athttp://relianceonscience.org. We outline several avenues for strategy research enabled by these new data. <italic>Managerial summary</italic>To what extent do firms rely on basic science in their R&D efforts? Several scholars have sought to answer this and related questions, but progress has been impeded by the difficulty of matching unstructured references in patents to published papers. We introduce an open‐access dataset of references from the front pages of patents granted worldwide to scientific papers published since 1800. Each patent‐paper linkage is assigned a confidence score, and we check a random sample of these confidence scores by hand in order to estimate both coverage (i.e., of the matches we should have found, what percentage did we find) and accuracy (i.e., of the matches we found, what percentage are correct). We outline several avenues for strategy research enabled by these new data. 

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3. CINES: Explore Citation Network and Event Sequences for Citation Forecasting

   https://doi.org/10.1145/3404835.3462903  

   He, Fang; Lee, Wang-Chien; Fu, Tao-Yang; Lei, Zhen (July 2021, Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021))

   Citations of scientific papers and patents reveal the knowledge flow and usually serve as the metric for evaluating their novelty and impacts in the field. Citation Forecasting thus has various applications in the real world. Existing works on citation forecasting typically exploit the sequential properties of citation events, without exploring the citation network. In this paper, we propose to explore both the citation network and the related citation event sequences which provide valuable information for future citation forecasting. We propose a novel Citation Network and Event Sequence (CINES) Model to encode signals in the citation network and related citation event sequences into various types of embeddings for decoding to the arrivals of future citations. Moreover, we propose a temporal network attention and three alternative designs of bidirectional feature propagation to aggregate the retrospective and prospective aspects of publications in the citation network, coupled with the citation event sequence embeddings learned by a two-level attention mechanism for the citation forecasting. We evaluate our models and baselines on both a U.S. patent dataset and a DBLP dataset. Experimental results show that our models outperform the state-of-the-art methods, i.e., RMTPP, CYAN-RNN, Intensity-RNN, and PC-RNN, reducing the forecasting error by 37.76% - 75.32%. 

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4. Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective

   https://doi.org/10.1109/TEM.2020.2966171  

   Huang, Ying; Zhu, Fujin; Porter, Alan L.; Zhang, Yi; Zhu, Donghua; Guo, Ying (January 2020, IEEE Transactions on Engineering Management)

   Technological innovation is a dynamic process that spans the lifecycle of an idea, from scientific research to production. Within this process, there are few key innovations that significantly impact a technology’s development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this paper, we present a framework for identifying the technology’s main evolutionary pathway of a technology. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology’s development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Last, we apply integrated approaches of main path analysis (MPA) -- namely global main path analysis and global key-route main analysis -- for extracting technological trajectories at different technological stages. We illustrate this approach with Dye-Sensitized Solar Cells (DSSCs), a low-cost solar cell belonging to the group of thin film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision-makers manage the technological stages of their innovation processes more effectively. 

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5. Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks

   https://doi.org/10.3389/frma.2020.600382  

   Kandimalla, Bharath; Rohatgi, Shaurya; Wu, Jian; Giles, C. Lee (February 2021, Frontiers in Research Metrics and Analytics)

   null (Ed.)

   Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using nine million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro- F 1 measure of 0.76 with F 1 of individual subject categories ranging from 0.50 to 0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers. 

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