Cortext platform
At Cortext, our goal is to empower researchers in the social sciences and humanities by promoting advanced qualitative-quantitative mixed methods. Our primary focus is on studies about the dynamics of science, technology and innovation, and about the roles of knowledge and expertise in societies.
We understand the move towards digital humanities and computational methods not as addressing a technological gap for the social sciences, but rather as entailing entirely new assemblages between its disciplines and those of modern statistics and computer sciences. We work to tackle ever more complex research problems and deal with the profusion of new and diverse sources of information without losing sight of the situatedness and reflexivity required of studies of human societies.
Cortext is hosted by the LISIS research unit at Gustave Eiffel University, and was launched by French institutes IFRIS and INRAE, receiving their continued support.
Cortext Manager
Cortext Manager is our current main attraction, a publicly available web service providing data analysis methods curated and developed by our team of researchers and engineers.
You upload a textual corpus in order to analyse its discourse, names, categories, citations, places, dates etc, with methods for science/controversy/issue mapping, distant reading, document clustering, geo-spatial and network visualizations, and more.
You can jump straight to Cortext Manager and create an account, but we strongly suggest taking a look at the Documentation and Tutorials as you start your journey.
Latest journal articles employing our instruments
Journal Articles
2022
Milia, Matias Federico; Giralt, Ariadna Nebot; Arvanitis, Rigas
Local emergence, global expansion: understanding the structural evolution of a bi-lingual national research landscape Journal Article
In: Scientometrics, 2022.
@article{Milia2022,
title = {Local emergence, global expansion: understanding the structural evolution of a bi-lingual national research landscape},
author = {Matias Federico Milia and Ariadna Nebot Giralt and Rigas Arvanitis},
url = {https://link.springer.com/article/10.1007/s11192-022-04403-9},
doi = {10.1007/s11192-022-04403-9},
year = {2022},
date = {2022-06-21},
urldate = {2022-06-21},
journal = {Scientometrics},
abstract = {Research institutions organize their scientific activities in an increasingly diverse landscape. In matters of global interest, research relies on an ever-more cross-disciplinary background, which reveals intriguing questions concerning the local dynamics vs. global audiences. This paper proposes new methodological tools to assess, from a strategic perspective, the evolution of a given research landscape. It relies on the Global Research Institute of Paris’ recent experience, a new interdisciplinary Institute focusing on globalization topics beyond the usual economic meaning. The Institute leans on a broad and diverse set of research units of the Université de Paris and relates to the broad landscape of social sciences in France. This article charts the evolution of French authors’ scientific publications on the Institute’s thematic interests in French and English. It focuses on the structural features of the debate, namely the volume, the underlying historical semantic structure, and its main thematic domains. The paper offers significant evidence to understand knowledge circulation dynamics and links that non-speaking countries’ scientific literature builds with the English one.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Franco, Sebastián Fernández; Graña, Juan M; Flacher, David; Rikap, Cecilia
Producing and using artificial intelligence: What can Europe learn from Siemens’s experience? Journal Article
In: Competition & Change, vol. 0, pp. 1–30, 2022.
@article{Franco2022,
title = {Producing and using artificial intelligence: What can Europe learn from Siemens’s experience?},
author = {Sebastián Fernández Franco and Juan M Graña and David Flacher and Cecilia Rikap},
url = {https://www.researchgate.net/profile/Juan-Grana-2/publication/360759657_Producing_and_using_artificial_intelligence_What_can_Europe_learn_from_Siemens's_experience/links/62a739f955273755ebe9963b/Producing-and-using-artificial-intelligence-What-can-Europe-learn-from-Siemenss-experience.pdf},
doi = {10.1177/10245294221097066},
year = {2022},
date = {2022-06-10},
urldate = {2022-06-10},
journal = {Competition & Change},
volume = {0},
pages = {1–30},
abstract = {This paper examines the innovation strategy of Siemens, a key player in Europe’s digital economy, by performing network and lexical analyses using data derived from Siemens’s patents and scientific publications since 1998. We observe that the company’s innovation efforts evolved from a broader attempt to develop internal information and communication technology (ICT) capabilities – alongside its historical industrial priorities – to a strategy focused on developing artificial intelligence (AI) for sector-specific and niche applications (such as life and medical sciences). As a result, it became dependent on tech giants’ clouds for accessing more general AI services and digital infrastructure. We build on the intellectual monopoly literature focusing on the effects of tech giants on other leading corporations, to analyse Siemens’s experience. By abandoning the development of general ICT and given the emergence of tech giants as digital economy intellectual monopolies, we show that Siemens is risking its technological autonomy towards these big tech companies. Our results provide clues to understand the challenges faced by Europe and its firms in relation to US and Chinese tech giants.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Crépel, Maxime; Cardon, Dominique
Robots vs algorithmes. Prophétie et critique dans la représentation médiatique des controverses de l’IA. Journal Article
In: Réseaux, vol. 2022/2-3, iss. 232-233, pp. 129-167, 2022.
@article{Crépel2022,
title = {Robots vs algorithmes. Prophétie et critique dans la représentation médiatique des controverses de l’IA.},
author = {Maxime Crépel and Dominique Cardon},
url = {https://medialab.github.io/carnet-algopresse/#/publication/fr/},
doi = {10.3917/res.232.0129},
year = {2022},
date = {2022-06-02},
urldate = {2022-06-02},
journal = {Réseaux},
volume = {2022/2-3},
issue = {232-233},
pages = {129-167},
abstract = {Signée par Elon Musk, Stephen Hawking, Steve Wozniak et Noam Chomsky, une lettre ouverte publiée en 2015 alerte sur les risques existentiels auxquels l’humanité doit faire face en raison des nouveaux développements de l’IA. Elle constitue un moment clé de la réactivation d’un ensemble de ressources narratives et normatives visant à porter sur la place publique un débat critique relatif aux progrès de l’IA. Par vagues successives, les promesses et les risques de l’intelligence artificielle sont venus occuper de façon spectaculaire la discussion publique. À partir de méthode de TAL appliquée à un large corpus de la presse anglo-saxonne, cet article montre que le thème des algorithmes et de l’IA occupe un espace croissant dans la sphère médiatique depuis 5 ans. Le corpus se structure autour de deux espaces sémantiques qui constituent deux régimes dominants de critique, l’un fondé sur les injustices produites par les algorithmes et l’autre sur les peurs de l’autonomie de l’IA et des robots. L’analyse comparée de ces espaces montre qu’ils mobilisent des agents technologiques et humains, des troubles et une temporalité distincts. En développant un discours critique sur les méfaits de ces technologies, la sphère médiatique contribue à forger l’opinion publique mais aussi à définir les formes d’acceptabilité de ces agents calculateurs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hackenburg, Kobi; Brady, William J.; Tsakiris, Manos
Mapping moral language on U.S. presidential primary campaigns reveals rhetorical networks of political division and unity Journal Article
In: 2022.
@article{Hackenburga2022,
title = {Mapping moral language on U.S. presidential primary campaigns reveals rhetorical networks of political division and unity},
author = {Kobi Hackenburg and William J. Brady and Manos Tsakiris},
url = {https://osf.io/wn5rx},
doi = {10.31219/osf.io/wn5rx},
year = {2022},
date = {2022-05-23},
urldate = {2022-05-23},
abstract = {During political campaigns, candidates use rhetoric to advance competing visions and assessments of their country. Research reveals that the moral language used in this rhetoric can significantly influence citizens’ political attitudes and behaviors; however, the moral language actually used in the rhetoric of elites during political campaigns remains understudied. Using a dataset of every tweet (N = 139,412) published by 39 U.S. presidential candidates during the 2016 and 2020 primary elections, we extracted moral language and constructed network models illustrating how candidates’ rhetoric is semantically connected. These network models yielded two key discoveries. First, we find that party affiliation clusters can be reconstructed solely based on the moral words used in candidates’ rhetoric. Within each party, popular moral values are expressed in highly similar ways, with Democrats emphasizing careful and just treatment of individuals and Republicans emphasizing in-group loyalty and respect for social hierarchies. Second, we illustrate the ways in which outsider candidates like Donald Trump can separate themselves during primaries by using moral rhetoric that differs from their parties’ common language. Our findings demonstrate the functional use of strategic moral rhetoric in a campaign context and show that unique methods of text network analysis are broadly applicable to the study of campaigns and social movements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
NotesVIEW ALL
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Long trends on twitter: intertemporal clusters combining hashtags and terms on Scientometrics, Altmetrics, Bibliometrics and Science Of Science
Long trends on twitter: inter-temporal clusters combining hashtags and terms, for all tweets on Scientometrics, Altmetrics, Bibliometrics and Science Of Science from Jan. 2017 to dec. 2021, on a semester base. Query used to extract tweets: lang:en (Scientometrics OR “ScienceOfScience” OR “Science Of Science” OR “Altmetrics” OR “altmetric” OR “bibliometrics” OR “bibliometric” OR “citation metrics” […]
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Présenter CorTexT Manager en 2 minutes
Cortext Manager est une application web construite par des chercheurs et par des ingénieurs à destination de chercheurs en sciences humaines et sociales, au plus près des questions portées par les chercheurs qui nous entourent et par notre communauté d’utilisateurs. Cette application web peut produire un grand nombre d’analyses différentes qui ont trait aux champs […]
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Analysis of the scientific production that mentioned the use of CorText Manager
There are two ways to understand what CorTexT Manager is. The first one is to look at what has been achieved in terms of methods, tools and therefore lines of code. The second one is studied below, by analyzing (here with CorTexT Manager) what academic users have published using… CorTexT Manager. Our study of the […]
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10 years of CorText Manager v2
It took us more than 10 years to come with CorText Manager version 2 as it is now! Behind the scenes CorText Manager begun with a first version in 2009. More than thirty contributors has worked directly or indirectly on the two versions, year after year. All the ideas, inspirations, all this accumulation of pieces […]
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RISIS Training: Thematic and spatial analysis of technologies using CorText Manager and RISIS patent database
One of the best CorText Manager training courses was organized and offered by the RISIS project. Here is the program of this training which lasted 3 days: Monday 08/11/21 14h-16h30: Session 1 Session 1a: Introduction on patent analysis (60’) Introductory lecture session • Welcoming introduction (Philippe Larédo) 5’ • Type of patents documents (Antoine Schoen) […]
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Early 2021 CorText Manager training sessions
CorText organized a series of training workshops on CorText Manager and its methods in January 2021! These workshops were imagined as a staircase with three successive steps : Session 1: Introduction Session 2: Method comparisons Session 3: Research questions and work on user’s corpus For these sessions, the subject chosen for the demonstrations and exercises […]
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Seminar and workshop during the Summer School of PPGCI IBICT UFRJ, Rio de Janeiro – 03/2020
In March 2020, the LabEx SITES post-doctoral researcher, Ale Abdo, traveled to Rio de Janeiro and São Paulo to organize two trainings on textual analysis and on a new method he developed and integrated at the CorText Infrastructure, as well as to participate in discussions on open and citizen science in Brazil, including the discussion […]
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A CorText Manager distance training session in the framework of the nanocellulose project – Grenoble, June 2020
For complementing the RISIS access requested (to Leiden publications DB and RISIS patent DB) by the GAEL laboratory (UMR INRAE, CNRS, UGA, INPG), in the framework of a research project on nanocellulose, the CorText team has provided , in June and July 2020, an advanced training on the use of CorText. After setting up of […]
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