Frédérique Bordignon

This paper examines how the Journal of the American Chemical Society (JACS) displays notices of correction and retraction, and how their status is reflected across various venues. With a corpus of 1083 editorial notices, we first show that even on the JACS website, the original source, there are mistakes and inaccuracies. Additionally, our study demonstrates some improvements in certain contexts in comparison to earlier studies, as well as significant variations between platforms (bibliographic databases and open access archives). But it also reveals that the same types of issues still remain, including the lack of accurate information close to the updated publications, and the lack of a two-way link between notices and original publications. This preliminary research seeks to provide an overview of what constitutes the scientific record and what it means to correct it, in order to avoid the spread of unsubstantiated claims by ill-informed readers.

National audience

A defining feature of the impact of the COVID-19 pandemic on scholarly communication has been the brief and intense surge in the production of preprints. This has had significant impacts on the ways in which new research findings have been reported and communicated more broadly and the role played by abstracts in highlighting the meaning and value of new research. Based on a study of the language deployed in the abstracts of recently published COVID-19 preprints, two NanoBubbles members, Fréd...

International audience

International audience

Le véhicule autonome dans les discours médiatisés : présentation des méthodes d'analysed'articles de presse et de tweets

This document is the DMP for the NanoBubbles project (ERC Synergy Grant). The project focuses on how, when and why science fails to correct itself. To understand how the correction of science works or fails, the NanoBubbles project combines approaches from the natural sciences, engineering (natural language processing) and humanities and social sciences (linguistics, sociology, philosophy and history of science). See https://nanobubbles.hypotheses.org for more information.

International audience

The VACOM project originated from the DGITM’s Intelligent Transport Mission questioning public perceptions of autonomous vehicles. What expectations and uncertainties does public opinion formulate in this period coming before the first sales of autonomous vehicles? The methodological choice was to analyze how autonomous vehicles are represented and staged in the media. Discourses were collected in the so-called "mainstream" newspaper and Internet media, and on Twitter's so-called "social" media, between December 2017 and May 2018. A body composed of articles published in national newspapers between 2012 and 2018 has been added to the current body for a diachronic analysis. Papers presented at the last five editions of the Transportation Research Board annual conference constituted a fourth body. The objective of this collection is to bring quantitative and qualitative knowledge regarding the way autonomous vehicles are treated in the media: how much information is disseminated on this subject via these channels? Who is behind this information? What problematic frameworks are the most frequently found?The report first discusses theoretical and methodological options. The second and third parts of the report present the main findings related to the analysis of the press body and of the Twitter body. The fourth part puts the two communication areas into perspective and considers the processes of production-diffusion-consumption of information.

Background: A platform called Opalia has been built to propose free access to all publications about a laboratory for a given range of years. This platform makes indexing of a corpus of a scientific article of a given lab. But in the French research system, a lab includes researchers from different organizations in the same unit generally called. UMR. Authors can write their laboratory names differently. Aim: Sorting a set of labels that is noisy can be seen as a binary classification into positives and leave negatives strings. We propose to use a cascade processing with the help of tagging some positive strings to build a relevant space of features that helps classification into good labels.