Great piece by jeffreybrainard.bsky.social with comments from others www.science.org/content/arti...
27.08.2025 19:16 β π 2 π 0 π¬ 0 π 0@danielacuna.bsky.social
Assoc. Prof. Computer Science at the University of Colorado, Boulder. Prev iSchool, Syracuse University. Postdoc Northwestern University and Ability Lab. PhD Computer Science UMN, Twin Cities
Great piece by jeffreybrainard.bsky.social with comments from others www.science.org/content/arti...
27.08.2025 19:16 β π 2 π 0 π¬ 0 π 0Can AI help identify high-volume, low-quality, βquestionableβ scientific journals (which some, controversially, call #predatoryjournals )? Authors of this new study emphasize aiding not replacing human evaluators of these journals. @science.org www.science.org/content/arti...
27.08.2025 18:56 β π 14 π 6 π¬ 0 π 1Take a look at the full paper:
- science.org/doi/10.1126/...
I started this project 5 years ago. Paper with my former students Han Zhuang and Lizhen Liang
Disclaimer: I am the founder of
ReviewerZero AI (www.reviewerzero.ai),
where we help with research integrity issues.
A few takeaways:
- the AI of course still makes mistakes, but we believe those mistakes are worth it
- predictions should be part of a triage system with experts. we make our predictions "interpretable", aligning with DOAJ guidelines
- But AI for integrity is here to stay
graph showing large grow of publications in questionable journals
Graph showing growth in citations
Graph showing distribution of countries in our prediction
Applied to journals that haven't been vetted before , we predict more than 1000 new potentially questionable journals.
They have collectively published over 500K articles, cited millions of times, and acknowledged major funders in US, China, and Japan
Table that shows good agreement between experts, DOAJ guidelines, and algorithmic predictions
We tried several methods, including simple regression, random forest and deep learning.
- Bibliometric features alone: PRC AUC β0.64
- Combined model: PRC AUC β0.79
Agreement with DOAJ guidelines and expert reviewers was strong, though false positives remain.
Overview of the system, which combines multiple kinds of signals including the website, visual features, and bibliometric features
We trained models on ~15,000 journals labeled by DOAJ (12,869 legitimate vs 2,536 removed). Features included:
- Website content (editorial boards, policies)
- Website design (HTML structure, screenshots)
- Bibliometrics (citations, author metrics)
Article front page: https://www.science.org/doi/10.1126/sciadv.adt2792
π¨New paperπ¨
Open access has expanded scienceβs reach but also fueled the rise of "questionable" journals. Manual vetting canβt keep pace with thousands of titles and bad actors who adapt quickly. Wrong incentives too strong.
In a new Science Advances paper we ask: can AI help?
that makes sense! π§
21.11.2024 00:02 β π 2 π 0 π¬ 0 π 0Who are these hundreds of people who started following me recently? I mean, hi π, but how did you find me without any advertisement from my part? π This place is starting to look great.
20.11.2024 17:56 β π 3 π 0 π¬ 2 π 0NotebookLM podcast version of the paper. Pretty good! notebooklm.google.com/notebook/d75... (need a Google account)
02.10.2024 19:15 β π 1 π 0 π¬ 0 π 0Excited to share a new article with Han Zhuang in Quantitative Studies of Science! "Incorporating costs and benefits to the evaluation of uncertain research results," we propose a unified framework for deciding when to continue research, even if it might be wrong π€―. direct.mit.edu/qss/article/...
02.10.2024 19:09 β π 2 π 1 π¬ 1 π 0Tenure/tenure-track faculty positions in NLP in CS. Looking for people in science of science! Super interdisciplinary dept, great place to work and grow, amazing location. Learn more jobs.colorado.edu/jobs/JobDeta... Feel free to reach out and ask about the position and details.
25.10.2023 19:37 β π 19 π 17 π¬ 0 π 1