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@article{Gourlet2024,

title = {Reclaiming artificial intelligence accounts: A plea for a participatory turn in artificial intelligence inquiries},

author = {Pauline Gourlet and Donato Ricci and Maxime Crépel},

url = {https://journals.sagepub.com/doi/pdf/10.1177/20539517241248093},

doi = {0.1177/20539517241248093},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

journal = {Big Data & Society},

abstract = {How to participate in artificial intelligence otherwise? Put simply, when it comes to technological developments, participation is either understood as public debates with non-expert voices to anticipate risks and potential harms, or as a way to better design technical systems by involving diverse stakeholders in the design process. We advocate for a third path that considers participation as crucial to problematise what is at stake and to get a grip on the situated developments of artificial intelligence technologies.

This study addresses how the production of accounts shape problems that arise with artificial intelligence technologies.

Taking France as a field of study, we first inspected how media narratives account for the entities and issues of artificial intelligence, as reported by the national press over the last decade. From this inspection, we identified four genres and described their performative effects. We then conducted a participatory inquiry with 25 French artificial intelligence practitioners’ to ground artificial intelligence in situated experiences and trajectories. These experiential accounts enabled a plural problematisation of artificial intelligence, playing with the geometries of artificial intelligence and its constituencies, while diversifying and thickening its problems.

To conclude, we discuss how participatory inquiries, through experiential and plural accounts offer a refreshing weaving of artificial intelligence problems into the fabric of its deployments. Our participatory approach seeks to re-politicise artificial intelligence from practitioners’ situated experiences, by making the ongoing relationships between past trajectories, current frictions and future developments tangible and contestable, opening avenues to contribute otherwise.},

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@article{Xiao2023,

title = {A Comprehensive Bibliometric Analysis of Big Data in Entrepreneurship Research},

author = {Anran Xiao and Yong Qin and Zeshui Xu and Marinko Skare},

url = {https://www.inzeko.ktu.lt/index.php/EE/article/view/30643},

doi = {https://doi.org/10.5755/j01.ee.34.2.30643 },

issn = {1392-2785},

year  = {2023},

date = {2023-04-28},

journal = {Engineering Economics},

volume = {34},

issue = {2},

pages = {175–192},

abstract = {Big data technology has been widely used in entrepreneurial research in recent years. To explore the development trend and fundamental characteristics of big data in entrepreneurship (BDIE) research, we conduct a comprehensive bibliometric analysis of BDIE based on 541 publications between 1993 and 2020 from Web of Science. On the one hand, this paper focuses on some essential characteristics of the BDIE publications, such as categories, citation, H-index, and the most cited publications. On the other hand, visual scientific maps are presented by bibliometric tools, i.e., Bibliometrix, VOS viewer, and CorTexT Manager, showing relationships between publications and knowledge structure in BDIE research. Finally, hot topics in current studies, knowledge, and limitations are discussed, guiding scholars to explore new research directions. This paper provides a relatively broad perspective for applying BDIE research, which contributes to understanding the evolution of BDIE research and inspires scholars in related fields.},

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}

@article{Rikap2022,

title = {Becoming an intellectual monopoly by relying on the national innovation system: the State Grid Corporation of China's experience},

author = {Cecilia Rikap},

url = {https://www.sciencedirect.com/science/article/pii/S004873332100264X},

doi = {10.1016/j.respol.2021.104472},

issn = {0048-7333},

year  = {2022},

date = {2022-05-01},

urldate = {2022-05-01},

journal = {Research Policy},

volume = {51},

number = {4},

pages = {104472},

abstract = {This paper examines the origins of global leaders under intellectual monopoly capitalism. State Grid Corporation of China (SGCC), the leading firm in artificial intelligence applications for the energy sector, became an intellectual monopoly relying heavily on China's national innovation system –particularly public research organizations and public funding, and innovation and energy policies. SGCC is unique because it did not rely on technology transfer from global leaders, unlike other national champions from developing or emerging countries. We provide evidence that contributes to thinking that SGCC first became a national intellectual monopoly and only afterwards expanded that monopoly globally. We empirically study SGCC's innovation networks. We proxy them using big data techniques to analyze the content, co-authors and co-owners of its publications and patents. Results also suggest that SGCC is capturing intellectual rents from its increasingly transnational and technologically diverse innovation networks by leveraging its national innovation system.},

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@article{Kabudi2021,

title = {AI-enabled adaptive learning systems: A systematic mapping of the literature},

author = {Tumaini Kabudi and Ilias Pappas and Dag Håkon Olsen},

url = {https://www.sciencedirect.com/science/article/pii/S2666920X21000114},

doi = {https://doi.org/10.1016/j.caeai.2021.100017},

issn = {2666-920X},

year  = {2021},

date = {2021-03-31},

urldate = {2021-03-31},

journal = {Computers and Education: Artificial Intelligence},

volume = {2},

pages = {100017},

abstract = {Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.},

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}

@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},

urldate = {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{Hu2017,

title = {Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization},

author = {Jiming Hu and Yin Zhang},

doi = {10.1007/s11192-017-2383-1},

year  = {2017},

date = {2017-05-01},

urldate = {2017-05-01},

journal = {Scientometrics},

volume = {112},

pages = {91–109},

abstract = {Big Data is a research field involving a large number of collaborating disciplines. Based on bibliometric data downloaded from the Web of Science, this study applies various social network analysis and visualization tools to examine the structure and patterns of interdisciplinary collaborations, as well as the recently evolving overall pattern. This study presents the descriptive statistics of disciplines involved in publishing Big Data research; and network indicators of the interdisciplinary collaborations among disciplines, interdisciplinary communities, interdisciplinary networks, and changes in discipline communities over time. The findings indicate that the scope of disciplines involved in Big Data research is broad, but that the disciplinary distribution is unbalanced. The overall collaboration among disciplines tends to be concentrated in several key fields. According to the network indicators, Computer Science, Engineering, and Business and Economics are the most important contributors to Big Data research, given their position and role in the research collaboration network. Centering around a few important disciplines, all fields related to Big Data research are aggregated into communities, suggesting some related research areas, and directions for Big Data research. An ever-changing roster of related disciplines provides support, as illustrated by the evolving graph of communities.},

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@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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@article{venturini2014three,

title = {Three maps and three misunderstandings: A digital mapping of climate diplomacy},

author = {Tommaso Venturini and Nicolas Baya-Laffite and Jean-Philippe Cointet and Ian Gray and Vinciane Zabban and Kari De Pryck},

url = {https://doi.org/10.1177/2053951714543804},

doi = {10.1177/2053951714543804},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Big Data & Society},

volume = {1},

number = {2},

pages = {1-19},

publisher = {SAGE Publications Sage UK: London, England},

abstract = {This article proposes an original analysis of the international debate on climate change through the use of digital methods. Its originality is twofold. First, it examines a corpus of reports covering 18 years of international climate negotiations, a dataset never explored before through digital techniques. This corpus is particularly interesting because it provides the most consistent and detailed reporting of the negotiations of the United Nations Framework Convention on Climate Change. Second, in this paper we test an original approach to text analysis that combines automatic extractions and manual selection of the key issue-terms. Through this mixed approach, we tried to obtain relevant findings without imposing them on our corpus. The originality of our corpus and of our approach encouraged us to question some of the habits of digital research and confront three common misunderstandings about digital methods that we discuss in the first part of the article (section ‘Three misunderstandings on digital methods in social sciences’). In addition to reflecting on methodology, however, we also wanted to offer some substantial contribution to the understanding of UN-framed climate diplomacy. In the second part of the article (section ‘Three maps on climate negotiations’) we will therefore introduce some of the preliminary results of our analysis. By discussing three visualizations, we will analyze the thematic articulation of the climatic negotiations, the rise and fall of these themes over time and the visibility of different countries in the debate. },

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