分析11年巴西智能系统会议，揭示研究趋势与学术影响力分布。
Eleven Years of BRACIS: A Meta-Scientific Study of the Brazilian Conference on Intelligent Systems
- 基于论文、引用和全文数据，构建完整研究档案
- 大模型研究占比从0升至2024年的19%，计算机视觉与优化仍为主流
- 顶尖论文贡献27%引用，开放性实践显著提升但多数论文仍难获取
本文对2015至2025年共1,066篇巴西智能系统会议（BRACIS）录用论文进行元科学研究，整合来自DBLP的元数据、谷歌学术6,765次引用及全文信息。研究发现：大语言模型相关研究从2020年前为零，增长至2024年占全部论文的19%；机器学习、计算机视觉与优化仍是基础方向。作者结构呈倒置沙漏状，2,623名作者中80.5%仅参与一次会议，机构重复投稿率接近作者率三倍。引用高度集中，前1%论文贡献27%总引用量。开放实践持续改善，论文附带可复现资源比例从2015年8.9%增至2023年57.3%；且有arXiv预印本的论文平均引用更高。由于会议论文集受IEEE与Springer付费墙限制，仅有7.4%的论文发布预印本，多数研究成果难以被无机构访问者获取。
原文摘要 · Abstract (English)
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.
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