'Special issue-ization' as a growth and revenue strategy: Reproduction by the "big five" and the risks for research integrity.

The exponential growth of MDPI and Frontiers over the last decade has been powered by their extensive use of special issues. The "special issue-ization" of journal publishing has been particularly associated with new publishers and seen as potentially "questionable." Through an extended case-study analysis of three journals owned by one of the "big five" commercial publishers, this paper explores the risks that this growing use of special issues presents to research integrity. All three case-study journals show sudden and marked changes in their publication patterns. An analysis of special issue editorials and retraction notes was used to determine the specifics of special issues and reasons for retractions. Descriptive statistics were used to analyse data. Findings suggest that these commercial publishers are also promoting special issues and that article retractions are often connected to guest editor manipulation. This underlies the threat that "special issue-ization" presents to research integrity. It highlights the risks posed by the guest editor model, and the importance of extending this analysis to long-existing commercial publishers. The paper emphasizes the need for an in-depth examination of the underlying structures and political economy of science, and a discussion of the rise of gaming and manipulation within higher education systems.

Large language models are changing landscape of academic publications. A positive transformation?

The advent of large language models (LLMs) based on neural networks marks a significant shift in academic writing, particularly in medical sciences. These models, including OpenAI's GPT-4, Google's Bard, and Anthropic's Claude, enable more efficient text processing through transformer architecture and attention mechanisms. LLMs can generate coherent texts that are indistinguishable from human-written content. In medicine, they can contribute to the automation of literature reviews, data extraction, and hypothesis formulation. However, ethical concerns arise regarding the quality and integrity of scientific publications and the risk of generating misleading content. This article provides an overview of how LLMs are changing medical writing, the ethical dilemmas they bring, and the possibilities for detecting AI-generated text. It concludes with a focus on the potential future of LLMs in academic publishing and their impact on the medical community.

Taking it back: A pilot study of a rubric measuring retraction notice quality.

The frequency of scientific retractions has grown substantially in recent years. However, thus far there is no standardized retraction notice format to which journals and their publishers adhere voluntarily, let alone compulsorily. We developed a rubric specifying seven criteria in order to judge whether retraction notices are easily and freely accessible, informative, and transparent. We mined the Retraction Watch database and evaluated a total of 768 retraction notices from two publishers (Springer and Wiley) over three years (2010, 2015, and 2020). Per our rubric, both publishers tended to score higher on measures of openness/availability, accessibility, and clarity as to why a paper was retracted than they did in: acknowledging institutional investigations; confirming whether there was consensus among authors; and specifying which parts of any given paper warranted retraction. Springer retraction notices appeared to improve over time with respect to the rubric's seven criteria. We observed some discrepancies among raters, indicating the difficulty in developing a robust objective rubric for evaluating retraction notices.

Identification of retracted publications and completeness of retraction notices in public health.

OBJECTIVES: Retraction is intended to be a mechanism to correct the published body of knowledge when necessary due to fraudulent, fatally flawed, or ethically unacceptable publications. However, the success of this mechanism requires that retracted publications be consistently identified as such and that retraction notices contain sufficient information to understand what is being retracted and why. Our study investigated how clearly and consistently retracted publications in public health are being presented to researchers. STUDY DESIGN AND SETTING: This is a cross-sectional study, using 441 retracted research publications in the field of public health. Records were retrieved for each of these publications from 11 resources, while retraction notices were retrieved from publisher websites and full-text aggregators. The identification of the retracted status of the publication was assessed using criteria from the Committee on Publication Ethics and the National Library of Medicine. The completeness of the associated retraction notices was assessed using criteria from Committee on Publication Ethics and Retraction Watch. RESULTS: Two thousand eight hundred forty-one records for retracted publications were retrieved, of which less than half indicated that the article had been retracted. Less than 5% of publications were identified as retracted through all resources through which they were available. Within single resources, if and how retracted publications were identified varied. Retraction notices were frequently incomplete, with no notices meeting all the criteria. CONCLUSIONS: The observed inconsistencies and incomplete notices pose a threat to the integrity of scientific publishing and highlight the need to better align with existing best practices to ensure more effective and transparent dissemination of information on retractions.

Estimating the productivity cost of administrative duties and work-related engagements from submission trends during COVID-19 pandemic lockdown.

During the initial months of the coronavirus disease 2019 pandemic, mBio experienced a large increase in the number of submissions, a phenomenon that was also observed for journals of different fields. Since most research laboratories were closed, this increase cannot reflect increased research activity. In this editorial, we propose that the increase in submissions reflected the release of a backlog of unpublished work following a reduction in work-related engagements including scientific travel, which in turn provides an estimate of the productivity costs of such activities on research output.

A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring.

OBJECTIVE: Investigate the use of advanced natural language processing models to streamline the time-consuming process of writing and revising scholarly manuscripts. MATERIALS AND METHODS: For this purpose, we integrate large language models into the Manubot publishing ecosystem to suggest revisions for scholarly texts. Our AI-based revision workflow employs a prompt generator that incorporates manuscript metadata into templates, generating section-specific instructions for the language model. The model then generates revised versions of each paragraph for human authors to review. We evaluated this methodology through 5 case studies of existing manuscripts, including the revision of this manuscript. RESULTS: Our results indicate that these models, despite some limitations, can grasp complex academic concepts and enhance text quality. All changes to the manuscript are tracked using a version control system, ensuring transparency in distinguishing between human- and machine-generated text. CONCLUSIONS: Given the significant time researchers invest in crafting prose, incorporating large language models into the scholarly writing process can significantly improve the type of knowledge work performed by academics. Our approach also enables scholars to concentrate on critical aspects of their work, such as the novelty of their ideas, while automating tedious tasks like adhering to specific writing styles. Although the use of AI-assisted tools in scientific authoring is controversial, our approach, which focuses on revising human-written text and provides change-tracking transparency, can mitigate concerns regarding AI's role in scientific writing.

COVID-19 Pandemic and Impact on Research Publications in Critical Care.

OBJECTIVES: The COVID-19 pandemic precipitated a significant transformation of scientific journals. Our aim was to determine how critical care (CC) journals and their impact may have evolved during the COVID-19 pandemic. We hypothesized that the impact, as measured by citations and publications, from the field of CC would increase. DESIGN: Observational study of journal publications, citations, and retractions status. SETTING: All work was done electronically and retrospectively. SUBJECTS: The top 18 CC journals broadly concerning CC, and the top 5 most productive CC journals on the SCImago list. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: For the top 18 CC journals and specifically Critical Care Medicine (CCM), time series analysis was used to estimate the trends of total citations, citations per publication, and publications per year by using the best-fit curve. We used PubMed and Retraction Watch to determine the number of COVID-19 publications and retractions. The average total citations and citations per publication for all journals was an upward quadratic trend with inflection points in 2020, whereas publications per year spiked in 2020 before returning to prepandemic values in 2021. For CCM total publications trend downward while total citations and citations per publication generally trend up from 2017 onward. CCM had the lowest percentage of COVID-related publications (15.7%) during the pandemic and no reported retractions. Two COVID-19 retractions were noted in our top five journals. CONCLUSIONS: Citation activity across top CC journals underwent a dramatic increase during the COVID-19 pandemic without significant retraction data. These trends suggest that the impact of CC has grown significantly since the onset of COVID-19 while maintaining adherence to a high-quality peer-review process.