scite: The next generation of citations

Rife, Sean C.; Rosati, Domenic; Nicholson, Joshua M.

doi:10.1002/leap.1379

Cited by 4 publications

(2 citation statements)

References 7 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whereas black-box machine learning approach have many strengths (e.g. Rife et al, 2021 ), ours is transparent and intuitive. Its transparency allows to easily identify terms that have field-specific meanings, which may be obfuscated in black-box approaches.…”

Section: Discussionmentioning

confidence: 99%

Investigating disagreement in the scientific literature

Lamers

Boyack

Larivière

et al. 2021

eLife

View full text Add to dashboard Cite

Disagreement is essential to scientific progress but the extent of disagreement in science, its evolution over time, and the fields in which it happens remain poorly understood. Here we report the development of an approach based on cue phrases that can identify instances of disagreement in scientific articles. These instances are sentences in an article that cite other articles. Applying this approach to a collection of more than four million English-language articles published between 2000 and 2015 period, we determine the level of disagreement in five broad fields within the scientific literature (biomedical and health sciences; life and earth sciences; mathematics and computer science; physical sciences and engineering; and social sciences and humanities) and 817 meso-level fields. Overall, the level of disagreement is highest in the social sciences and humanities, and lowest in mathematics and computer science. However, there is considerable heterogeneity across the meso-level fields, revealing the importance of local disciplinary cultures and the epistemic characteristics of disagreement. Analysis at the level of individual articles reveals notable episodes of disagreement in science, and illustrates how methodological artifacts can confound analyses of scientific texts.

show abstract

Section: Discussionmentioning

confidence: 99%

Investigating disagreement in the scientific literature

Lamers

Boyack

Larivière

et al. 2021

eLife

View full text Add to dashboard Cite

show abstract

“…What would be more useful would be to use modern technology to identify among all the topic‐compatible reviewers those who would be most likely to produce unbiased reviews. For example, large‐scale citation context analysis, using machine‐learning techniques (e.g., Pradhan et al, 2019; Rife et al, 2021), could be used to determine if potential reviewers have tended to cite the authors of a manuscript, or others working on the same topic, systematically in a negative or positive way in the past, or if their views have tended to be neutral and balanced. Any abnormal pattern of citation between the authors and potential reviewers, which might be indicative of the existence of a ‘citation club’ or ‘cartel’, could also be identified using network analysis tools (e.g., Fister Jr. et al, 2016).…”

Section: Possible Remediesmentioning

confidence: 99%

Objectivity of the peer‐review process: Enduring myth, reality, and possible remedies

Baveye

2021

Learned Publishing

View full text Add to dashboard Cite

Key points The ‘replication crisis’ in science raises serious questions about the objectivity and reliability of the peer‐review process. Much of the literature, contributed on the topic in the past by former editors, has focused on the role of reviewers, and their possible biases. However, experience in practice shows that editors also contribute significantly, at different levels, to the lack of objectivity of peer‐review. Various techniques, including network analysis and machine learning, can be implemented to remedy the situation and restore the gatekeeping role of peer review.

show abstract

Artificial intelligence for literature reviews: opportunities and challenges

Bolaños,

Salatino,

Osborne

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

This paper presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates prior research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research. We highlight three primary research challenges: integrating advanced AI solutions, such as large language models and knowledge graphs, improving usability, and developing a standardised evaluation framework. We also propose best practices to ensure more robust evaluations in terms of performance, usability, and transparency. Overall, this review offers a detailed overview of AI-enhanced SLR tools for researchers and practitioners, providing a foundation for the development of next-generation AI solutions in this field.

show abstract

scite: The next generation of citations

Cited by 4 publications

References 7 publications

Investigating disagreement in the scientific literature

Investigating disagreement in the scientific literature

Objectivity of the peer‐review process: Enduring myth, reality, and possible remedies

Artificial intelligence for literature reviews: opportunities and challenges

Contact Info

Product

Resources

About