A publishing infrastructure for AI-assisted academic authoring.
Ontology highlight
ABSTRACT:
SUBMITTER: Pividori M
PROVIDER: S-EPMC9900745 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
A publishing infrastructure for AI-assisted academic authoring.
Pividori Milton M Greene Casey S CS
bioRxiv : the preprint server for biology 20230123
In this work we investigate how models with advanced natural language processing capabilities can be used to reduce the time-consuming process of writing and revising scholarly manuscripts. To this end, we integrate large language models into the Manubot publishing ecosystem to suggest revisions for scholarly text. We tested our AI-based revision workflow in three case studies of existing manuscripts, including the present one. Our results suggest that these models can capture the concepts in th ...[more]
Similar Datasets
Project description:ObjectiveInvestigate the use of advanced natural language processing models to streamline the time-consuming process of writing and revising scholarly manuscripts.Materials and methodsFor 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.ResultsOur 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.ConclusionsGiven 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.
| S-EPMC11339502 | biostudies-literature
Project description:Academic journals provide a key quality-control mechanism in science. Yet, information asymmetries and conflicts of interests incentivize scientists to deceive journals about the quality of their research. How can honesty be ensured, despite incentives for deception? Here, we address this question by applying the theory of honest signaling to the publication process. Our models demonstrate that several mechanisms can ensure honest journal submission, including differential benefits, differential costs, and costs to resubmitting rejected papers. Without submission costs, scientists benefit from submitting all papers to high-ranking journals, unless papers can only be submitted a limited number of times. Counterintuitively, our analysis implies that inefficiencies in academic publishing (e.g., arbitrary formatting requirements, long review times) can serve a function by disincentivizing scientists from submitting low-quality work to high-ranking journals. Our models provide simple, powerful tools for understanding how to promote honest paper submission in academic publishing.
| S-EPMC7901761 | biostudies-literature
Project description:BackgroundFew details are known about open-access surgery journals that solicit manuscripts via E-mail. The objectives of this cross-sectional study are to compare solicitant surgery journals with established journals and to characterize the academic credentials and reasons for publication of their authorship.MethodsWe identified publishers who contacted the senior author and compared their surgery journals with 10 top-tier surgical journals and open-access medical journals. We assessed the senior authorship of articles published January 2017-March 2017 and utilized a blinded survey to determine motivations for publication.ResultsThroughout a 6-week period, 110 E-mails were received from 29 publishers distributing 113 surgery journals. Compared with established journals, these journals offered lesser publication fees, but also had lesser PubMed indexing rates and impact factors (all P < .002). Professors, division chiefs, and department chairs were the senior authors of nearly half of US-published papers and spent ≈$83,000 to publish 117 articles in journals with a median impact factor of 0.12 and a 33% PubMed indexing rate. Survey responses revealed a dichotomy as 43% and 57% of authors published in these journals with and without knowledge of their solicitant nature, respectively. The most commonly reported reasons for submission included waived publication fees (50%), invitation (38%), and difficulty publishing elsewhere (12%).ConclusionDespite their sparse PubMed indexing and low impact factors, many senior academic faculty publish in solicitant surgery journals. This study highlights the importance for the academic surgical community to be cognizant of the quality of a journal when reviewing the literature for research and evidence-based practice.
| S-EPMC6074048 | biostudies-literature
Project description:Researchers give papers for free (and often actually pay) to exploitative publishers who make millions off of our articles by locking them behind paywalls. This discriminates not only against the public (who are usually the ones that paid for the research in the first place), but also against the academics from institutions that cannot afford to pay for journal subscriptions and the 'scholarly poor'. I explain exploitative and ethical publishing practices, highlighting choices researchers can make right now to stop exploiting ourselves and discriminating against others.
| S-EPMC5428494 | biostudies-literature
Project description:BackgroundThe academic publishing world is changing significantly, with ever-growing numbers of publications each year and shifting publishing patterns. However, the metrics used to measure academic success, such as the number of publications, citation number, and impact factor, have not changed for decades. Moreover, recent studies indicate that these metrics have become targets and follow Goodhart's Law, according to which, "when a measure becomes a target, it ceases to be a good measure."ResultsIn this study, we analyzed >120 million papers to examine how the academic publishing world has evolved over the last century, with a deeper look into the specific field of biology. Our study shows that the validity of citation-based measures is being compromised and their usefulness is lessening. In particular, the number of publications has ceased to be a good metric as a result of longer author lists, shorter papers, and surging publication numbers. Citation-based metrics, such citation number and h-index, are likewise affected by the flood of papers, self-citations, and lengthy reference lists. Measures such as a journal's impact factor have also ceased to be good metrics due to the soaring numbers of papers that are published in top journals, particularly from the same pool of authors. Moreover, by analyzing properties of >2,600 research fields, we observed that citation-based metrics are not beneficial for comparing researchers in different fields, or even in the same department.ConclusionsAcademic publishing has changed considerably; now we need to reconsider how we measure success.
| S-EPMC6541803 | biostudies-literature
Project description:ObjectiveAcademics are under great pressure to publish their research, the rewards for which are well known (tenure, promotion, grant funding, professional prestige). As open access publishing gains acceptance as a publishing option, researchers may choose a "predatory publisher." The purpose of this study is to investigate the motivations and rationale of pharmacy and nursing academics in the United States to publish in open access journals that may be considered "predatory."MethodsA 26-item questionnaire was programmed in Qualtrics and distributed electronically to approximately 4,500 academic pharmacists and nurses, 347 of whom completed questionnaires (~8%). Pairwise correlations were performed followed by a logistic regression to evaluate statistical associations between participant characteristics and whether participants had ever paid an article processing fee (APF).ResultsParticipants who had published more articles, were more familiar with predatory publishing, and who were more concerned about research metrics and tenure were more likely to have published in open access journals. Moderate to high institutional research intensity has an impact on the likelihood of publishing open access. The majority of participants who acknowledged they had published in a predatory journal took no action after realizing the journal was predatory and reported no negative impact on their career for having done so.ConclusionThe results of this study provide data and insight into publication decisions made by pharmacy and nursing academics. Gaining a better understanding of who publishes in predatory journals and why can help address the problems associated with predatory publishing at the root.
| S-EPMC9782588 | biostudies-literature
Project description:BackgroundTo inform training program development and curricular initiatives, quantitative descriptions of the disciplinary training of research teams publishing in top-tier clinical and epidemiological journals are needed. Our objective was to assess whether interdisciplinary academic training and teamwork of authors publishing original research in 15 top-tier journals varied by year of publication (2000/2010/2020), type of journal (epidemiological/general clinical/specialty clinical), corresponding author gender, and time since the corresponding author completed formal training relative to the article publication date (<5/≥5 years).Methods and findingsWe invited corresponding authors of original research articles to participate in an online survey (n = 103; response rate = 8.3% of 1240 invited authors). In bivariate analyses, year of publication, type of journal, gender, and recency of training were not significantly associated with interdisciplinary team composition, whether a co-author with epidemiological or biostatistical training was involved in any research stage (design/analysis/interpretation/reporting), or with participants' confidence in their own or their co-authors epidemiological or biostatistical expertise (p > 0.05 for each comparison). Exceptions were participants with more recent epidemiological training all had co-author(s) with epidemiological training contribute to study design and interpretation, and participants who published in 2020 were more likely to report being extremely confident in their epidemiological abilities.ConclusionsThis study was the first to quantify interdisciplinary training among research teams publishing in epidemiological and clinical journals. Our quantitative results show research published in top-tier journals generally represents interdisciplinary teamwork and that interdisciplinary training may provide publication type options. Our qualitative results show researchers view interdisciplinary training favorably.
| S-EPMC9337644 | biostudies-literature
Project description:IntroductionIn academia, many institutions use journal article publication productivity for making decisions on tenure and promotion, funding grants, and rewarding stellar scholars. Although non-alphabetical sequencing of article coauthoring by the spelling of surnames signals the extent to which a scholar has contributed to a project, many disciplines in academia follow the norm of alphabetical ordering of coauthors in journal publications. By assessing business academic publications, this study investigates the hypothesis that author alphabetical ordering disincentivizes teamwork and reduces the overall quality of scholarship.MethodsTo address our objectives, we accessed data from 21,353 articles published over a 20-year period across the four main business subdisciplines. The articles selected are all those published by the four highest-ranked journals (in each year) and four lower-ranked journals (in each year) for accounting, business technology, marketing, and organizational behavior. Poisson regression and binary logistic regression were utilized for hypothesis testing.ResultsThis study finds that, although team size among business scholars is increasing over time, alphabetical ordering as a convention in journal article publishing disincentivizes author teamwork. This disincentive results in fewer authors per publication than for publications using contribution-based ordering of authors. Importantly, article authoring teamwork is related to article quality. Specifically, articles written by a single author typically are of lesser quality than articles published by coauthors, but the number of coauthors exhibits decreasing returns to scale-coauthoring teams of one to three are positively related to high-quality articles, but larger teams are not. Alphabetical ordering itself, however, is positively associated with quality even though it inhibits teamwork, but journal article coauthoring has a greater impact on article quality than does alphabetical ordering.ConclusionsThese findings have important implications for academia. Scholars respond to incentives, yet alphabetical ordering of journal article authors conflicts with what is beneficial for the progress of academic disciplines. Based on these findings, we recommend that, to drive the highest-quality research, teamwork should be incentivized-all fields should adopt a contribution-based journal article author-ordering convention and avoid author ordering based upon the spelling of surnames. Although this study was undertaken using articles from business journals, its findings should generalize across all academia.
| S-EPMC8099113 | biostudies-literature
Project description:IntroductionThis mixed-methods study evaluates the impact of AI-assisted language learning on Chinese English as a Foreign Language (EFL) students' writing skills and writing motivation. As artificial intelligence (AI) becomes more prevalent in educational settings, understanding its effects on language learning outcomes is crucial.MethodsThe study employs a comprehensive approach, combining quantitative and qualitative methods. The quantitative phase utilizes a pre-test and post-test design to assess writing skills. Fifty EFL students, matched for proficiency, are randomly assigned to experimental (AI-assisted instruction via ChatGPT) or control (traditional instruction) groups. Writing samples are evaluated using established scoring rubrics. Concurrently, semi-structured interviews are conducted with a subset of participants to explore writing motivation and experiences with AI-assisted learning.ResultsQuantitative analysis reveals significant improvements in both writing skills and motivation among students who received AI-assisted instruction compared to the control group. The experimental group demonstrates enhanced proficiency in various aspects of writing, including organization, coherence, grammar, and vocabulary. Qualitative findings showcase diverse perspectives, ranging from recognition of AI's innovative instructional role and its positive influence on writing skills and motivation to concerns about contextual accuracy and over-reliance. Participants also reflect on the long-term impact and sustainability of AI-assisted instruction, emphasizing the need for ongoing development and adaptation of AI tools.DiscussionThe nuanced findings offer a comprehensive understanding of AI's transformative potential in education. These insights have practical implications for practitioners and researchers, emphasizing the benefits, challenges, and the evolving nature of AI's role in language instruction.
| S-EPMC10754989 | biostudies-literature
Project description:This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.
| S-EPMC11762411 | biostudies-literature