scite: The next generation of citations

Abstract

Key points While the importance of citation context has long been recognized, simple citation counts remain as a crude measure of importance. Providing citation context should support the publication of careful science instead of headline‐grabbing and salami‐sliced non‐replicable studies. Machine learning has enabled the extraction of citation context for the first time, and made the classification of citation types at scale possible.

Investigating disagreement in the scientific literature

“…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.…”

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

“…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).…”