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For more than 10 years, researchers routinely complained that, because of the fast expansion of the scholarly literature, it was becoming very challenging for them to keep abreast of novel developments in even a very narrow portion of their discipline (e.g., Baveye 2014). At the same time, journal editors have experienced increasing difficulties to recruit reviewers (Siegel and Baveye 2010). Over the last few years, the situation does not appear to have improved significantly (West and Bergstrom 2021; Baveye 2021a, 2021b). The scholarly literature keeps expanding at an exponential rate. According to some estimates, 5.14 million articles were published during 2022, sizeably more than the 4.18 million published just 4 years earlier (Curcic 2023). More than ever, with conflicting demands on their time for teaching, supervising undergraduate and graduate students, reviewing for journals, or writing numerous proposals to compete for limited funding, researchers generally find it virtually impossible to devote as many hours as would be needed to read articles of direct interest to them in sufficient depth.

Not surprisingly in this context, a significant effort has unfolded to review and synthesise relatively large bodies of literature and make their content more readily accessible to researchers and policy-makers. In recent years, tens of thousands of systematic reviews and especially of “meta-analyses” have been written. The staggering scale of the endeavour is evinced by the fact that the article of Page et al. (2021), proposing revised reporting guidelines for meta-analyses, has already been cited over 79,000 times, in only 3 years, according to Google Scholar (https://scholar.google.com; Last retrieved, January 29, 2025). Because it is proving time-consuming to stay abreast even of meta-analyses in virtually all disciplines, a trend is currently emerging of synthesising meta-analyses via what has been referred to as “second-order” meta-analyses (e.g., Schmidt and Oh 2013; Bergquist et al. 2023), or of carrying out “overviews of systematic reviews” (Lunny et al. 2024). In 2023 alone, more than 7000 articles referred to these practices, according to Google Scholar.

No doubt part of the appeal of the meta-analysis method over the years has been its original description as a robust technique with a strong statistical foundation (Glass 1976; Shadish and Lecy 2015). Nevertheless, implementations of the method in practice have been the object of very strong criticisms, in particular in research on education (Abrami, Cohen, and d'Apollonia 1988; Ropovik, Adamkovic, and Greger 2021), medicine (Haidich 2010; Hedin et al. 2016; Chapman 2020), plant ecology (Koricheva and Gurevitch 2014), agronomy (Philibert, Loyce,

Little consistency exists in how individuals enter scholarly publishing, let alone advance their careers. More transparency and documentation can help increase diversity in an industry that wrestles with its privilege. In this article, we report on a project initiated by three publishing industry associations to aggregate, normalise, and analyse public job postings and internal position descriptions in scholarly publishing. After gathering more than 1000 unique descriptions, a group of knowledgeable volunteers qualitatively coded them. Researchers from the University of Michigan checked for data consistency and analysed the job description corpus. Preliminary visualisations highlight the skills that suit potential applicants for various publishing positions and the skills that are most important to build for advancement. The findings can inform the development of products to make publishing a more equitable industry, such as interactive tools to match individuals with types of publishing jobs, well-formed template positions, and training programs that address skills gaps.

Farber, S. 2024. “ Enhancing Peer Review Efficiency: A Mixed-Methods Analysis of Artificial Intelligence-Assisted Reviewer Selection Across Academic Disciplines.” Learned Publishing 37: e1638. https://doi.org/10.1002/leap.1638.

In the section “Ethical considerations in AI-assisted peer review,” an incorrect reference was included in the following sentence:

“The ‘black box’ nature of some machine learning algorithms can make it difficult to understand how reviewer recommendations are generated, potentially undermining trust in the peer-review process (Horbach & Halffman, 2020).” This should have read “The ‘black box’ nature of some machine learning algorithms can make it difficult to understand how reviewer recommendations are generated, potentially undermining trust in the peer-review process (Pillai, 2024).”

Correct reference: Pillai, V. 2024. “Enhancing Transparency and Understanding in AI Decision-Making Processes.” Iconic Research and Engineering Journals 8, no. 1: 168–172.

We apologise for this error.

Open repositories were created to enhance access and visibility of scholarly publications, driven by open science ideals emphasising transparency and accessibility. However, they lack mechanisms to update the status of corrected or retracted publications, posing a threat to the integrity of the scholarly record. To explore the scope of the problem, a manually verified corpus was examined: we extracted all the entries in the Crossref × Retraction Watch database for which the publication date of the corrected or retracted document ranged from 2013 to 2023. This corresponded to 24,430 entries with a DOI, which we use to query Unpaywall and identify their possible indexing in HAL, an open repository (second largest institutional repository worldwide). In most cases (91%), HAL does not mention corrections. While the study needs broader scope, it highlights the necessity of improving the role of open repositories in correction processes with better curation practices. We discuss how harvesting operations and the interoperability of platforms can maintain the integrity of the entire scholarly record. Not only will the open repositories avoid damaging its reliability through ambiguous reporting, but on the contrary, they will also strengthen it.

This study aims to evaluate the influence of the China Sci-Tech Journal Excellence Action Plan (CJEAP) on the development of Chinese science and technology (sci-tech) journals. The performance of bibliometric indicators of these journals before and after the implementation of this plan is examined. In particular, a discipline richness algorithm is employed to evaluate whether and how the funding plan affected the disciplinal coverage of publications. The results show that the influence of CJEAP funding in sci-tech journals published in China is not evenly distributed. Highly funded journals appear to have expanded both the range of research areas and disciplines, and the article volume, while poorly funded journals mainly focus on attracting manuscripts with higher scientific impact but display less expanded disciplinal range. New journals funded by CJEAP are characterised with high scientific levels focusing on highly sophisticated fields, but initially featuring a relatively small article volume. Notably, a positive relationship exists between the international collaboration rate and citation ranking score, and thus the expansion of article volume with manuscripts mainly from Chinese scholars may not be conducive for enhancing the international influence of journals published in China. In summary, our results indicate that CJEAP funding has elicited powerful influence in promoting sci-tech journals published in China, suggesting continuous funding support should be both necessary and efficient for further promoting the development of sci-tech journals published in China.

In this paper, we clarify the notions of plagiarism and self-plagiarism and show that a rather straightforward observation about these notions has important implications for the admissibility of recycling research outputs. The key point is that contextual variation must be taken into account in normative assessments of recycling research outputs, and we illustrate this with some examples. In particular, we apply the analysis in order to dissolve a disagreement about the proper handling of submissions to conferences. Some researchers are comfortable with sending the same contribution to several conferences, while others find that unacceptable and a clear deviation from good research practise. We take a closer look at the arguments regarding whether it is acceptable or not to make the same conference contribution more than once, including the argument that submitting the same contribution more than once would amount to self-plagiarism. We argue that contextual variation must be taken into account, in accordance with our previous analysis, and conclude that whether or not a duplication of a conference contribution deviates from good research practise depends on what significance is ascribed to it in the specific case. We conclude with some practical recommendations, emphasising for example, the importance of being explicit and clear on this point, and encourage conference organisers to provide opportunities to specify relevant facts in the submission.

There are ample reasons why papers might get rejected by peer-reviewed journals, and the experience can be, especially for those who have had little experience, sobering. When papers get rejected a number of times, that may signal that there are problems with the paper (e.g., weak methodology or lack of robust analyses), that it is insufficiently developed, is poorly written, or that it is too topic-specific and needs to find an appropriate niche journal. In the case of a single or multiple rejections, whenever there is feedback from a journal, as well as reasons for rejection, this provides a useful signal for improving the paper before it is resubmitted to another journal. This article examines literature related to the rejection of papers in academic journals, encompassing the opinions and experiences offered by authors, as well as advice suggested by editors, allowing readers and authors who experience rejections to reflect on the possible reasons that may have led to that outcome. Many papers related to this topic were published as editorials or opinions, offering advice on how to improve aspects of a submitted paper in order to increase its chances of acceptance.

Presents the results of a study of the impact of artificial intelligence on early career researchers (ECRs). An important group to study because their millennial mindset may render them especially open to AI. We provide empirical data and a validity check of the numerous publications providing forecasts and prognostications. This interview-based study—part of the Harbingers project on ECRs—covers a convenience sample of 91 ECRs from all fields and seven countries using both qualitative and quantitative data to view the AI experience, engagement, utility, attitudes and representativeness of ECRs. We find that: (1) ECRs exhibit mostly limited or moderate levels of experience; (2) in regard to engagement and usage there is a divide with some ECRs exhibiting little or none and others enthusiastically using AI; (3) ECRs do not think they are unrepresentative when compared to their colleagues; (4) ECRs who score highly on these measures tend to be computer scientists, but not exclusively so; (5) the main concerns regarding AI were around authenticity, especially plagiarism; (6) a major attraction of AI is the automation of ‘wordsmithing’; the process and technique of composition and writing.

The aim of this paper is to highlight the situation whereby content generated by the large language model ChatGPT is appearing in peer-reviewed papers in journals by recognized publishers. The paper demonstrates how to identify sections that indicate that a text fragment was generated, that is, entirely created, by ChatGPT. To prepare an illustrative compilation of papers that appear in journals indexed in the Web of Science and Scopus databases and possessing Impact Factor and CiteScore indicators, the SPAR4SLR method was used, which is mainly applied in systematic literature reviews. Three main findings are presented: in highly regarded premier journals, articles appear that bear the hallmarks of the content generated by AI large language models, whose use was not declared by the authors (1); many of these identified papers are already receiving citations from other scientific works, also placed in journals found in scientific databases (2); and, most of the identified papers belong to the disciplines of medicine and computer science, but there are also articles that belong to disciplines such as environmental science, engineering, sociology, education, economics and management (3). This paper aims to continue and add to the recently initiated discussion on the use of large language models like ChatGPT in the creation of scholarly works.