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@article{nokey,

title = {Towards integrating joint action research: Developmental and evolutionary perspectives on co-representation},

author = {F.M. Miss and J.E.C. Adriaense and J.M. Burkart},

url = {https://www.sciencedirect.com/science/article/pii/S0149763422004134},

doi = {10.1016/j.neubiorev.2022.104924},

issn = {0149-7634},

year  = {2022},

date = {2022-10-22},

journal = {Neuroscience & Biobehavioral Reviews},

volume = {143},

pages = {104924},

abstract = {Joint action has increasingly become a key topic to understand the emergence of the human mind. The phenomenon is closely linked to several theoretical concepts, such as shared intentionality, which are difficult to operationalize empirically. We therefore employ a paradigm-driven, bottom-up approach, and as such discuss co-representing the partner’s and one’s own actions as key mechanism for joint action. After embedding co-representation in the broader landscape of related theoretical concepts, we review neurobiological, ontogenetic, and phylogenetic studies, with a focus on whether co-representation and its flexible deployment should be construed as a low- or high-level cognitive process. The empirical findings convergently suggest that co-representation does not require strong inhibitory skills or mentalistic understanding and occurs automatically. Moreover, more cooperative species are better at flexibly suppressing co-representation when required for cooperation success, and frequently rely on cooperation markers, such as mutual gaze. We thus contribute to closing the current gap between theoretical concepts related to joint action research and their empirical investigation, and end by highlighting additional approaches for doing so.},

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pubstate = {published},

tppubtype = {article}

}

@article{Zhao2021,

title = {Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles},

author = {Yi Zhao and Lifan Liu and Chengzhi Zhang},

doi = {https://doi.org/10.1016/j.techfore.2021.121344},

year  = {2021},

date = {2021-11-08},

urldate = {2021-11-08},

journal = {Technological Forecasting and Social Change},

volume = {121344},

abstract = {The outbreak of coronavirus disease 2019 (COVID-19) has had a significant repercussion on the health, economy, politics and environment, making coronavirus-related issues more complicated and difficult to adequately address by relying on a single field. Interdisciplinary research can provide an effective solution to complex issues in the related field of coronavirus. However, whether coronavirus-related research becomes more interdisciplinary still needs corroboration. In this study, we investigate interdisciplinary status of the coronavirus-related fields via the COVID-19 Open Research Dataset (CORD-19). To this end, we calculate bibliometric indicators of interdisciplinarity and apply a co-occurrence analysis method. The results show that co-occurrence relationships between cited disciplines have evolved dynamically over time. The two types of co-occurrence relationships, Immunology and Microbiology & Medicine and Chemical Engineering & Chemistry, last for a long time in this field during 1990–2020. Moreover, the number of disciplines cited by coronavirus-related research increases, whereas the distribution of disciplines is uneven, and this field tends to focus on several dominant disciplines such as Medicine, Immunology and Microbiology, Biochemistry, Genetics and Molecular Biology. We also measure the disciplinary diversity of COVID-19 related papers published from January to December 2020; the disciplinary variety shows an upward trend, while the degree of disciplinary balance shows a downward trend. Meanwhile, the comprehensive index 2Ds demonstrates that the degree of interdisciplinarity in coronavirus field decreases between 1990 and 2019, but it increases in 2020. The results help to map the interdisciplinarity of coronavirus-related research, gaining insight into the degree and history of interdisciplinary cooperation.},

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pubstate = {published},

tppubtype = {article}

}

@article{Xu2021,

title = {Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization},

author = {Xin Xu and Jiming Hu and Xiaoguang Lyu and Huang He and Cheng Xingyu },

doi = {10.2196/23562 },

year  = {2021},

date = {2021-01-11},

journal = {JMIR Medical Informatics},

abstract = {The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications.},

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pubstate = {published},

tppubtype = {article}

}

@article{Rikap2020,

title = {Amazon: A story of accumulation through intellectual rentiership and predation },

author = {Cecilia Rikap},

doi = {https://doi.org/10.1177/1024529420932418},

year  = {2020},

date = {2020-06-17},

journal = {Competition & Change},

abstract = {This article elaborates on intellectual monopoly theory as a form of predation and rentiership using Amazon as a case study. By analysing Amazon’s financial statements, scientific publications and patents, we show that Amazon’s economic power heavily relies on its systematic innovations and capacity to centralize and analyse customized data that orients its business and innovations. We demonstrate how Amazon’s innovation activities have evolved over time with growing importance of technologies related to data and machine learning. We also map Amazon’s innovation networks with academic institutions and companies. We show how Amazon appropriates intellectual rents from these networks and from technological cooperation with other intellectual monopolies. We argue that Amazon, as other data-driven monopolies, predates value from suppliers and third-party companies participating in its platform. One striking characteristic of Amazon is the low rate of reported profits. The centrality of innovations leads us to suggest an alternative calculation that shows that Amazon’s profits are not as low as they appear in Annual Reports. We also argue that lower profits are coherent with Amazon’s rentiership and predatory strategy since they contribute to the avoidance of accusations of excessive market power. Finally, the paper offers preliminary observations on: (i) the complementarities between financial and intellectual rentierism and (ii) how data-driven intellectual monopoly expands big corporations’ political power. Going beyond the specific case of Amazon, we thus contribute to a better understanding of the role of lead firms and power dynamics within innovation networks.},

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pubstate = {published},

tppubtype = {article}

}

@article{Rikap2019,

title = {Asymmetric Power of the Core: Technological Cooperation and Technological Competition in the Transnational Innovation Networks of Big Pharma},

author = {Cecilia Rikap},

url = {https://www.tandfonline.com/doi/abs/10.1080/09692290.2019.1620309},

year  = {2019},

date = {2019-06-26},

urldate = {2019-06-26},

journal = {Review of International Political Economy},

volume = {26},

number = {5},

pages = {987-1021},

abstract = {This article theoretically and empirically analyzes leader corporations’ innovation processes in contemporary capitalism. We highlight three characteristics: their transnational scope, the primacy of power or asymmetric relations exercised by leaders over the participants of their innovation circuits or networks, and the relevance of what we called technological competition and technological cooperation between leaders. Focusing on the latter, our theoretical contribution integrates the concepts of innovation circuit, global innovation network and modularity of knowledge production in order to elaborate a preliminary model for synthesizing leader’s technological competition and collaboration behaviors. This model is the general framework used for studying three big pharma’s innovation networks (Roche, Novartis and Pfizer). In particular, we study those networks by considering two outputs: scientific publications and patents. Network maps are constructed based on institutions’ co-occurrences, thus looking at who is co-authoring their publications and co-owning these corporations’ patents. We find that big pharmaceuticals co-produce together mainly generic knowledge modules, thus develop a strong technological cooperation. Simultaneously, to succeed in their technological competition they outsource stages of their innovation networks to subordinate institutions that, even if they contribute to achieve the innovation, will not be co-owners of the resulting patents, while big pharmaceuticals enjoy associated innovation rents.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shen2019,

title = {Detecting the Interdisciplinary Nature and Topic Hotspots of Robotics in Surgery: Social Network Analysis and Bibliometric Study},

author = {Lining Shen and Shimin Wang and Wei Dai and Zhiguo Zhang},

doi = {https://doi.org/10.2196/12625},

year  = {2019},

date = {2019-03-19},

journal = {Journal of Medical Internet Research},

volume = {21},

number = {3},

abstract = {Background: With the widespread application of a robot to surgery, growing literature related to robotics in surgery (RS) documents widespread concerns from scientific researchers worldwide. Although such application is helpful to considerably improve the accuracy of surgery, we still lack the understanding of the multidiscipline-crossing status and topic distribution related to RS. Objective: The aim of this study was to detect the interdisciplinary nature and topic hotspots on RS by analyzing the current publication outputs related to RS. Methods: The authors collected publications related to RS in the last 21 years, indexed by the Web of Science Core Collection. Various bibliometric methods and tools were used, including literature distribution analysis at the country and institution level and interdisciplinary collaboration analysis in the different periods of time. Co-word analysis was performed based on the keywords with high frequency. The temporal visualization bar presented the evolution of topics over time. Results: A total of 7732 bibliographic records related to RS were identified. The United States plays a leading role in the publication output related to RS, followed by Italy and Germany. It should be noted that the Yonsei University in South Korea published the highest number of RS-related publications. Furthermore, the interdisciplinary collaboration is uneven; the number of disciplines involved in each paper dropped from the initial 1.60 to the current 1.31. Surgery; Engineering; Radiology, Nuclear Medicine, and Medical Imaging; and Neurosciences and Neurology are the 4 core disciplines in the field of RS, all of which have extensive cooperation with other disciplines. The distribution of topic hotspots is in imbalanced status, which can be categorized into 7 clusters. Moreover, 3 areas about the evolution of topic were identified, namely (1) the exploration of techniques that make RS implemented, (2) rapid development of robotic systems and related applications in surgery, and (3) application of a robot to excision of tissues or organs targeted at various specific diseases. Conclusions: This study provided important insights into the interdisciplinary nature related to RS, which indicates that the researchers with different disciplinary backgrounds should strengthen cooperation to publish a high-quality output. The research topic hotspots related to RS are relatively scattered, which has begun to turn to the application of RS targeted at specific diseases. Our study is helpful to provide a potential guide to the direction of the field of RS for future research in the field of RS.},

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pubstate = {published},

tppubtype = {article}

}

@article{cambrosio2014big,

title = {Big Data and the Collective Turn in Biomedicine. How Should We Analyze Post-Genomic Practices?},

author = {Alberto Cambrosio and Pascale Bourret and Vololona Rabeharisoa and Michel Callon},

url = {http://www.tecnoscienza.net/index.php/tsj/article/view/178},

year  = {2014},

date = {2014-01-01},

journal = {TECNOSCIENZA: Italian Journal of Science & Technology Studies},

volume = {5},

number = {1},

pages = {11-42},

abstract = {We presently witness a profound transformation of the configuration of biomedical practices, as characterized by an increasingly collective dimension, and by a growing reliance on disruptive technologies that generate large amounts of data. We also witness a proliferation of biomedical databases, often freely accessible on the Web, which can be easily analyzed thanks to network analysis software. In this position paper we discuss how science and technology studies (S&TS) may cope with these developments. In particular, we examine a number of shortcomings of the notion of networks, namely those concerning: (a) the relation between agency and structural analysis; (b) the distinction between network clusters and collectives; (c) the (ac)counting strategies that fuel the networking approach; and (d) the privileged status ascribed to textual documents. This will lead us to reframe the question of the relations between S&TS and biomedical scientists, as big data offer an interesting opportunity for developing new modes of cooperation between the social and the life sciences, while avoiding the dichotomies – between the social and the cognitive, or between texts and practices – that S&TS has successfully managed to discard.},

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pubstate = {published},

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}