août 2025

Bibliodiversity of Small Academic Publishers: The Role of Open Access for Impact and Visibility
05/08/2025

Bibliodiversity of Small Academic Publishers: The Role of Open Access for Impact and Visibility

« Large bibliographic databases highlight tangible and symbolic differences regarding the standards of quality attached to them, underlining diverging incentive structures for small and large academic publishers. To assess the academic differences associated with these, we explore bibliometric data for small publishers’ journals from the Web of Science (WoS) and Scopus.…

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Matching Game Preferences Through Dialogical Large Language Models: A Perspective
04/08/2025

Matching Game Preferences Through Dialogical Large Language Models: A Perspective

« This perspective paper explores the future potential of « conversational intelligence » by examining how Large Language Models (LLMs) could be combined with GRAPHYP’s network system to better understand human conversations and preferences. Using recent research and case studies, we propose a conceptual framework that could make AI rea-soning transparent and traceable,…

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Perceptions on the adoption of Free/Open Source Software policies by a Scientific Institution: The case study of the NIH
04/08/2025

Perceptions on the adoption of Free/Open Source Software policies by a Scientific Institution: The case study of the NIH

« As the Open Science context evolves broadly and Scientific Institutions implement Open Access and Open Science policies, it is of interest to observe which are the issues that come into play when these Institutions attempt to contribute to contribute to make their research outputs visible, accessible and reusable. Thepresent work…

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Actes des 30es rencontres de la Société Francophone de Classification
04/08/2025

Actes des 30es rencontres de la Société Francophone de Classification

« In NLP, handling underrepresented topics is challenging, particularly in unsupervised tasks where clustering may fail to capture minority topics effectively. To address this, we propose an unsupervised data augmentation method that combines Gaussian Mixture Models (GMMs) and Large Language Models (LLMs). GMMs identify underrepresented clusters, while LLMs generate synthetic documents…

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