Beyond networks: Aligning qualitative and computational science studies

Cambrosio, Alberto; Cointet, Jean‐Philippe; Abdo, Alexandre Hannud

doi:10.1162/qss_a_00055

Cited by 10 publications

(9 citation statements)

References 56 publications

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“…Hence, in-depth analyses for our case: "social interaction showed by major patents documents subsets that are concerned how we support, store and technically process social interactions and games, which appears to be the primary tools in social interactions. To generalize this position, in a complement of traditional academic state of the art methods, we claim that addressing the patent literature stands for developing an in-depth analysis of the heterogeneous associations" (Cambrosio et al, 2020) that constitute (hyper)networks (Latour's heterogenesis of the translation-transformations) as well as insights into technology dynamics, societal embedding, and societal dynamics; texts can be analyzed "as assemblages of terms leading to specific ways of problematizing issues by defining the roles assigned to relevant entities (researchers, substances, tools, and technologies)". Underuse of patent documents may suppose historical consideration, perhaps ideology, but also a lack of tools to search databases.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Patents information for humanities research: Could there be something?

Reymond

2020

Iberoam J Sci Meas Comm

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Latour and co-authors proposed, in the Science and Technology Translation theory, to target the many SHS (Social and Human Science) questions addressed by social studies of sciences by considering, in complement to traditional academic matters, the complete social environment (political, economic or societal). Patents obviously are a potential primary information source to do so. We propose to extend this considering that recent changes have evolved in our capacity to do so. We propose three preliminary steps: (a) patent documents as providing a structured information source, (b) a patent database as a technical encyclopedia and (c) the recent expansion of the variety of uses and users in patent domains. We underline, furthermore, that minority research in the academic space does effectively use patent information, especially in SHS compared to other disciplines. We deliver an experiment to estimate the amount of data unconsidered by not questioning the huge database of the European Patent Office. By comparatively considering the terminology of the two branches of the Unesco thesaurus, namely the micro thesauri “Social and Human Sciences" and the “Information and Communication Science” branches, we evaluate a database response to the whole vocabulary. An in-depth analysis of one selected concept will complete the study. Results show that patent information may provide a quantity of documents for a wide range of academic research questions, from strategic to state of the art, and position advances aside from the Social Studies of Science. The free open source tool is also a way to practice digital humanities expected skills on real world corpora.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Patents information for humanities research: Could there be something?

Reymond

2020

Iberoam J Sci Meas Comm

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“…And, to our knowledge, the use of mixed methods has been non-existent to date. We thereby also respond to recent calls (Leydesdorff et al, 2020;Cambrosio et al, 2020) for a renewed dialogue between qualitative and quantitative/computational science studies, a dialogue that has begun to emerge in studies about synthetic biology or nanotechnology for instance, but been virtually non-existent regarding gene editing.…”

Section: Introductionmentioning

confidence: 96%

The Geographies and Politics of Gene Editing: Framing Debates Across Seven Countries

Meyer

Vergnaud

2021

Front. Polit. Sci.

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This article traces the contours and dynamics of the debates about the politics of gene editing. It does so by providing both a quantitative and qualitative analysis of the publications on the topic. We present a scientometric analysis of scientific publications; we discuss the geographies of gene editing by analysing the scales and spatial terms mobilised; and we undertake a lexicometric analysis of how debates are framed and the public is positioned. Our scientometric analysis of scientific articles shows that the governance and regulation of gene editing is discussed across an increasing range of disciplines and countries over the years. Along with this internationalisation and “transdisciplinarisation,” we see a qualitative shift in the “grounding” of the debate: while initially, authors tend to reflect about gene editing, in more recent years, there are increasing calls to act upon current knowledge. Across the countries we studied (the United States, the United Kingdom, Germany, China, Australia, Japan, and Canada) our lexicometric analysis shows only a few differences in terms of how gene editing is discussed. While the general framing of the debate is widely shared, the differences that we observe concern for instance the applications or benefits of gene editing and the ways in which the importance of involving the public is worded. We hold that bringing together multiple methods allows a rich and multifaceted discussion of the politics of gene editing, and that this opens up fertile dialogues between geography, sociology and political science.

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“…Our project is based on the methodology of structured content analysis, which seeks to systematically turn qualitative phenomenon into categorical and quantitative data (Riff et al, 2013). We draw on and situate our study within the growing efforts to bridge the fields of qualitative and quantitative science studies (Leydesdorff et al, 2020;Bowker, 2020;Cambrosio et al, 2020;Kang and Evans, 2020). Quantitative science studies often examines the outputs of science, such as analyzing bibliometrics and other already-quantitative trace data to understand how scientists' final products have been received within science and other institutions.…”

Section: Introductionmentioning

confidence: 99%

“Garbage in, garbage out” revisited: What do machine learning application papers report about human-labeled training data?

Geiger

Cope

et al. 2021

Quantitative Science Studies

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Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data. This study builds on prior work that investigated to what extent ‘best practices’ around labeling training data were followed in applied ML publications within a single domain (social media platforms). In this paper, we expand by studying publications that apply supervised ML in a far broader spectrum of disciplines, focusing on human-labeled data. We report to what extent a random sample of ML application papers across disciplines give specific details about whether best practices were followed, while acknowledging that a greater range of application fields necessarily produces greater diversity of labeling and annotation methods. Because much of machine learning research and education only focuses on what is done once a “ground truth” or “gold standard” of training data is available, it is especially relevant to discuss issues around the equally-important aspect of whether such data is reliable in the first place. This determination becomes increasingly complex when applied to a variety of specialized fields, as labeling can range from a task requiring little-to-no background knowledge to one that must be performed by someone with career expertise. Peer Review https://publons.com/publon/10.1162/qss_a_00144

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Beyond networks: Aligning qualitative and computational science studies

Cited by 10 publications

References 56 publications

Patents information for humanities research: Could there be something?

Patents information for humanities research: Could there be something?

The Geographies and Politics of Gene Editing: Framing Debates Across Seven Countries

“Garbage in, garbage out” revisited: What do machine learning application papers report about human-labeled training data?

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