Meta-Research: How problematic citing practices distort science

Retraction notices are expected to be transparent about entities accountable for retractions and their reasons for retraction. No previous research on retraction notices has investigated accountable entities other than authors of retracted publications and their reasons for retraction from a cross-disciplinary and a diachronic perspective. Drawing on a dataset of 7650 unique retraction notices published before 2020 and indexed in the Web of Science Core Collection, this study identified 457 retraction notices that held four types of non-author entities accountable. Journal authorities (i.e., editors and publishers) were found responsible for retractions in 62.14% of the retraction notices, followed by unidentifiable entities (26.91%), third parties (6.13%), and dual entities (i.e., both authors and journal authorities, 4.81%). The three most frequent reasons for retraction were republication of valid publications (36.76%), unspecified reasons (26.91%), and publication of unpublishable work (17.07%).Accountable non-author entities were more identifiable in the retraction notices published between 2010 and 2019 than in those published before 2010. Retraction notices in soft disciplines were more likely than those in hard disciplines to identify journal authorities as accountable entities.These findings offer implications for handling retractions more effectively and efficiently.

Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, authors summarize prior research in papers' related work sections, though this is scoped to support a single paper. A formative study found that while reading multiple related work paragraphs helps overview a topic, it is hard to navigate overlapping and diverging references and research foci. In this work, we design a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information. From a within-subjects user study (n=15), we found that scholars generate more coherent, insightful, and comprehensive topic outlines using Relatedly compared to a baseline paper list. CCS CONCEPTS• Human-centered computing → Graphical user interfaces.

Citing practices have long been at the heart of scientific reporting, playing both socially and epistemically important functions in science. While such practices have been relatively stable over time, recent attempts to develop automated citation recommendation tools have the potential to drastically impact citing practices. We claim that, even though such tools may come with tempting advantages, their development and implementation should be conducted with caution. Describing the role of citations in science’s current publishing and social reward structures, we argue that automated citation tools encourage questionable citing practices. More specifically, we describe how such tools may lead to an increase in: perfunctory citation and sloppy argumentation; affirmation biases; and Matthew effects. In addition, a lack of transparency of the tools’ underlying algorithmic structure renders their usage problematic. Hence, we urge that the consequences of citation recommendation tools should at least be understood and assessed before any attempts to implementation or broad distribution are undertaken.