Author : Parissa Mokhtabad Amrei
Open Science practices are shaping both science and policymaking. This thesis examines the visions of Open Science and their enactments through four empirical cases. It provides an understanding of what Open Science means in terms of infrastructures: in cases where Open Science practices exhibit infrastructuring efforts, where they reconfigure existing infrastructures, or where such infrastructuring efforts are not sustained.
Authors : Talip Gönülal, Ramazan Güçlü, Salih Güçlü
This exploratory study investigated the impact of artificial intelligence (AI) tools on academic publishing for non-native English-speaking researchers. Through a mixed-methods convergent parallel design, it examined how these scholars utilize AI tools, their perceived benefits, and concerns regarding AI’s influence on academic publishing.
Data were collected from 105 non-native English-speaking academics coming from 25 language backgrounds. Participants primarily employed AI tools for grammar improvement, writing style enhancement, and translation, while maintaining control over higher-level intellectual tasks such as organizing manuscripts.
Three key dimensions of the perceived impact of AI were identified in this study: the good, reducing linguistic inequalities by improving paper quality and decreasing language-related challenges; the bad, involving inaccurate or misleading AI suggestions, over-reliance on AI tools, and diminished engagement with manuscripts; and the ugly, characterized by failure to disclose AI use, lack of clear guidelines for responsible AI integration in research, homogenization of academic writing, and the emergence of new forms of inequality.
The study concluded with several recommendations for individual researchers, academic institutions, and publishers and journals to promote the ethical and effective use of AI in academic publishing.
URL : AI In Academic Publishing for Non-Native English Speakers: The Good, the Bot, and the Ugly
Authors : David Nicholas, David Clark, Abdullah Abrizah, John Akeroyd, Eti Herman, Jorge Revez, Blanca Rodríguez-Bravo, Marzena Swigon, Tatyana Polezhaeva, Anne Gere
The article presents the latest (2025) iteration of the Harbingers longitudinal project on early career researchers (ECRs), artificial intelligence (AI) and scholarly communications. In conversation with a purposive and diverse sample of more than 60 ECRs in six countries and numerous subjects, we present an evaluation of a pressing issue: what impact will AI have on their work and career?
An important issue is that widespread media speculation suggests that it is entry-level positions that will be hit hardest by AI. While ECRs were asked 50 plus questions during interviews, none were directly asked about changes to job security and employment prospects, yet much of relevance was volunteered in answering related AI questions.
Adding a new methodological dimension to the Harbingers project, we employed AI (NotebookLM) for an initial qualitative analysis of the interview data, with findings reviewed and corrected by the national interviewers. We conclude that AI is a double-edged sword which has huge potential as well as posing significant challenges.
The AI-assisted analysis proved effective at identifying broad themes, though human oversight was essential to capture nuance, differences between cohorts, and unusual cases. Finally, given that we were working with a select and relatively small sample to inform a larger study, the data should be seen as illuminating and filling a research lacuna, rather than a definitive result in a fast-changing field.
URL : Do Early Career Researchers Consider AI as an Opportunity or a Threat? A Pathfinding Study
Authors : Sanja Gidakovic, Heather Moulaison-Sandy, Jenny Bossalle
Introduction
Freely available standalone AI research assistants such as Elicit and Consensus are used by academics to find relevant literature. These systems rely extensively on freely available sources, including open access journal content. No baseline for understanding the level of quality of such journals used in these assistants has been carried out.
Method
A sample of 807 English-language journals from the Directory of Open Access Journals that became open access before 2021 was investigated for quality metrics using SCImago rankings and other defining characteristics and analysed in conjunction with the Directory data.
Analysis
Scimago journal ranking quartile scores were recorded for each of the journals. Descriptive statistics were produced using Excel, and visualizations using Tableau Public.
Results
Of our sample, over half were ranked in Scopus, and many were in quartile 1. Many university or small association journals were unranked.
Conclusions
AI research assistants may miss some high-quality open access content due to reliance on metrics. Commercial enterprises play a large role in sources used to produce content, effectively gatekeeping the process and potentially shaping the creation of new knowledge.
Autrice : Lison Burlat
Cet article interroge les effets ambivalents du déploiement, en France, de l’intelligence artificielle générative (IAg) sur deux activités professionnelles féminisées de « soutien à la recherche » : la traduction et l’édition de sciences humaines et sociales. Il s’inscrit dans une perspective croisant les travaux de sociologie des professions et du travail féminin face aux technologies et ceux de la traductologie féministe.
Une première partie souligne que l’IAg révèle des luttes de juridiction préexistantes entre chercheur·ses, éditrices et traductrices, à replacer dans un contexte socio-économique spécifique. Une seconde partie montre qu’éditrices et traductrices ne défendent pas à armes égales leur territoire professionnel dans ce contexte.
Le premier groupe, plus structuré, entend se saisir de l’IAg pour requalifier son activité. Le second, plus fragmenté et soumis aux évolutions de la demande, est au contraire déqualifié par la relégation à la post-édition, voire est évacué de la chaîne.
Authors : Andrés F. Castro Torres, Joan Giner-Miguelez, Mercè Crosas
The extent to which Artificial Intelligence (AI) can trigger generalized paradigm shifts in science is unclear. Although some of these technologies have revolutionized data collection and analysis in specific scientific fields such as Chemistry, their overall impact depends on the scope of adoption and the ways scholars use them.
In this study, we document substantial differences in the timing and extent of AI adoption across countries and scientific domains from 1960 to 2015. After 2015, we find generalized exponential growth in AI adoption, with the number of AI-supported works multiplying by at least four across all domains. The transformative nature of this rapid growth is less apparent and points to multiple challenges should adoption trends persist.
According to our analyses, AI-supported research is confined to very few topics with strong ties to Computer Science and conventional statistical frameworks, suggesting limited transformational potential in epistemological terms. AI-supported works are also associated with an unwarranted citation premium and exhibit substantially higher retraction rates than non-AI-supported works across most fields.
Geographically, AI adoption displays pronounced heterogeneity at the country level, along with an acceleration in the relevance of middle-income countries in Asia, from China and beyond.
Thus, the transformative capacity of AI in science remains largely untapped, and its rapid adoption underlines challenges in research openness, transparency, reproducibility, and ethics from a global perspective. We discuss how best research practices could boost the benefits of AI adoption and highlight fields and geographies where these trends warrant closer scrutiny.
Authors : Niliek Silva‑Alés, Antonio Perianes‑Rodríguez
There is a growing interest in studying the influence of funding on scientific progress. Through exploration of the connections between funding acknowledgements (FAs), which link research results to funding sources, science communication processes can be understood and their influence in the international context can be evaluated.
Such analyses become more complex when the projects involved have two or more funding sources. This study examines FAs that mention the Seventh Framework Programme (FP7) and tries to achieve a broader, fuller, more singular view than previous studies of FP7 by visualising co-funding networks and conducting a structural analysis of inter-agency relationships.
This is done using open sources that have been linked after exhaustive data cleansing and harmonisation and the assignment of unique identifiers. Compliance with the objectives of the three most visible, most productive programmes is also examined, and the geographical distribution of the agencies participating in co-funding networks is evaluated.
One intriguing result shows that the number of projects with associated publications has risen 21% thanks to FAs. Considerable differences between programmes are also revealed: IDEAS-ERC is the programme with the highest number of co-funder’s, and HEALTH is the programme with the densest, most cohesive network.
Lastly, it is found that a stronger commitment is required from all the actors involved in the course of co-funding and publication to ensure that the funding data provided is of the right quality to facilitate accurate, transparent, useful, full evaluations.