Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems
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Abstract (arXiv)
The Brazilian Conference on Intelligent Systems (BRACIS) is the main national venue for Artificial Intelligence research in Brazil, hosted by the Brazilian Computer Society since 2012 and publishing work from institutions across the country. Across eleven years, from 2015 to 2025, we build a per-paper record of all 1,066 accepted papers from DBLP metadata, 6,765 Google Scholar citations, and the paper full texts, and use it to ask what BRACIS publishes, who publishes it, and which work gets cited. Large Language Model research grows from zero before 2020 to 19% of papers in 2024, on top of a base of Machine Learning, Computer Vision, and Optimization work. The community is hourglass-shaped: 80.5% of 2,623 authors appear in a single edition, while institutions return at nearly three times the author rate. Citations are heavily concentrated, with the top 1% of papers carrying 27% of the total. Openness practices have grown, with artifact release rising from 8.9% of papers in 2015 to 57.3% in 2023, and we find a notable correlation between having an arXiv preprint and higher citation counts. Since proceedings sit behind IEEE and Springer paywalls and only 7.4% of papers have a preprint, most BRACIS work is hard to reach for readers without institutional access.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 63 references that are in the map. First posted on arXiv 26 Jul 2026.
Builds on
22 of 63 references.
References · 22
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- 2023Sabi'a: Portuguese Large Language Modelscited by 19
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- 2026Sabi'a-4 Technical Reportcited by 6
10 more
- 2014Power laws in citation distributions: Evidence from Scopuscited by 5
- 2017Popularity of arXiv.org within Computer Sciencecited by 5
- 2018A writer-independent approach for offline signature verification using deep convolutional neural networks featurescited by 5
- 2024Juru: Legal Brazilian Large Language Model from Reputable Sourcescited by 5
- 2016Authorship attribution via network motifs identificationcited by 4
- 2020Deep learning models for representing out-of-vocabulary wordscited by 4
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- 2022Gender Representation in Brazilian Computer Science Conferencescited by 1
- 2024Effects of Research Paper Promotion via ArXiv and Xcited by 1
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