Abstract The importance of evidence‐based policymaking is widely recognized, but how science influences policy remains insufficiently explored. This study aims to examine how policy documents cite research articles, thereby tracing the complex impact process of scientific research on policymaking. A conceptual model is proposed to classify four types of citation pathways by distinguishing between direct and indirect impacts and observing whether a reinforcement effect is present. To operationalize this model, we collected nearly 10 thousand policy documents related to artificial intelligence (AI) and over 1.6 million links between these policies and their referenced articles. A large‐scale data analysis and a case study were conducted. Results exhibit distinct citation pathways among specific types of institutions, geopolitical areas, and policy areas. Indirect influences emerge as an important mechanism. Research articles from EU countries primarily serve the policymaking of inter‐governmental organizations (IGOs) and the EU, while research articles from the USA significantly support both domestic and foreign policymaking. Notably, IGOs serve as key intermediaries, facilitating the indirect influence of research on policymaking. In addition, while the knowledge from the social sciences provides substantial support for policies in various areas, an increasing involvement of the natural sciences in the development of AI‐related policies is found.

Abstract The classification of research according to its aims has been a longstanding focus in the fields of quantitative science studies and R&D statistics. Since 1963, the Organization for Economic Co‐operation and Development (OECD) has employed a classical distinction among basic, applied, and experimental research. Building on this framework, our previous work highlighted the utility of differentiating between scientific and societal progress as two primary research objectives. This distinction enabled the quantitative analysis of scientific publication abstracts and the development of an automated method for large‐scale classification. In the current study, we systematically evaluate text classification techniques, including traditional text mining models, classification tools, BERT‐based language models, and decoder‐only large language models (LLMs) such as ChatGPT. Our findings show that the fine‐tuned GPT‐4o‐mini model performs the best among single‐model approaches. However, traditional and BERT‐based models outperform in certain fine‐grained classification tasks. Leveraging majority voting strategies to incorporate their strengths yields performance comparable to closed‐source GPT models. A case study on 10 biomedical journals further validates the method, demonstrating strong alignment between journal scopes, model predictions, and outputs generated by the fine‐tuned GPT‐4o‐mini model. These results highlight the robustness and practical effectiveness of the proposed methodology for nuanced research aim classification.

Abstract Social sciences are increasingly recognized as significant for building a sustainable world since the social perspective can assist researchers in other fields in navigating public controversy and designing more responsible interaction mechanisms between the natural and social systems. However, the question arises: to what extent do natural sciences rely on social science research in their studies? Examining life science publications from seven PLoS journals, this paper attempts to characterize the volume and trajectory of citations from life sciences to social sciences. We explore three core questions: To what extent do life sciences cite social sciences? What actors in the life sciences are citing social sciences? Which actors in the social sciences are being cited? Our analysis estimates social sciences influence 15%–19% of life science publications, contributing to 1.1%–1.5% of references in 2018. Social science citers are found across peripheral and central topics of life science disciplines. Cited social science publications exhibit various levels of interdisciplinarity and achieve the greatest citation impact among peers. Citations to social sciences are prevalent in both theoretically and methodologically oriented sections. We show empirically the increasing impact of social sciences on the development of the life sciences.

Abstract The purpose of this study is to investigate the validity of tweets about scientific publications as an indicator of societal impact by measuring the degree to which the publications are tweeted beyond academia. We introduce methods that allow for using a much larger and broader data set than in previous validation studies. It covers all areas of research and includes almost 40 million tweets by 2.5 million unique tweeters mentioning almost 4 million scientific publications. We find that, although half of the tweeters are external to academia, most of the tweets are from within academia, and most of the external tweets are responses to original tweets within academia. Only half of the tweeted publications are tweeted outside of academia. We conclude that, in general, the tweeting of scientific publications is not a valid indicator of the societal impact of research. However, publications that continue being tweeted after a few days represent recent scientific achievements that catch attention in society. These publications occur more often in the health sciences and in the social sciences and humanities.

Abstract This paper investigates different uses of the Journal Impact Factor (JIF) in national journal rankings and discusses the merits of supplementing metrics with expert assessment. Our focus is national journal rankings used as evidence to support decisions about the distribution of institutional funding or career advancement. The seven countries under comparison are China, Denmark, Finland, Italy, Norway, Poland, and Turkey—and the region of Flanders in Belgium. With the exception of Italy, top‐tier journals used in national rankings include those classified at the highest level, or according to tier, or points implemented. A total of 3,565 (75.8%) out of 4,701 unique top‐tier journals were identified as having a JIF, with 55.7% belonging to the first Journal Impact Factor quartile. Journal rankings in China, Flanders, Poland, and Turkey classify journals with a JIF as being top‐tier, but only when they are in the first quartile of the Average Journal Impact Factor Percentile. Journal rankings that result from expert assessment in Denmark, Finland, and Norway regularly classify journals as top‐tier outside the first quartile, particularly in the social sciences and humanities. We conclude that experts, when tasked with metric‐informed journal rankings, take into account quality dimensions that are not covered by JIFs.

The objective of this article is to further the study of journal interdisciplinarity, or, more generally, knowledge integration at the level of individual articles. Interdisciplinarity is operationalized by the diversity of subject fields assigned to cited items in the article's reference list. Subject fields and subfields were obtained from the L euven‐ B udapest ( ECOOM ) subject‐classification scheme, while disciplinary diversity was measured taking variety, balance, and disparity into account. As diversity measure we use a H ill‐type true diversity in the sense of J ost and L einster‐ C obbold. The analysis is conducted in 3 steps. In the first part, the properties of this measure are discussed, and, on the basis of these properties it is shown that the measure has the potential to serve as an indicator of interdisciplinarity. In the second part the applicability of this indicator is shown using selected journals from several research fields ranging from mathematics to social sciences. Finally, the often‐heard argument, namely, that interdisciplinary research exhibits larger visibility and impact, is studied on the basis of these selected journals. Yet, as only 7 journals, representing a total of 15,757 articles, are studied, albeit chosen to cover a large range of interdisciplinarity, further research is still needed.

In this article, the authors answer the question of whether the field of scientometrics/bibliometrics shares essential characteristics of “metrics” sciences. To achieve this objective, the citation network of seven selected metrics and their information environment is analyzed.

A new visual representation of the response time, i.e., the time elapsed between the publication year and the date of the first citation of a paper, is provided. This presentation can be used to detect and describe different paradigmatic types of reception speed for scientific journals.

A bibliometric method for analyzing and visualizing national research profiles is adapted to describe national preferences for publishing particular document types. Similarities in national profiles and national peculiarities are discussed based on the publication output of the 26 most active countries indexed in the Web of Science annual volume 2007.