开放科学，封闭模型：资金如何塑造科学中的人工智能
Open Science, Closed Models: How Funding Shapes AI in Science
AI总结：
本研究分析104,226篇论文，发现资金来源影响科学中基础模型的参与方式：公共资金促进开放权重模型使用，行业云积分和合作偏向封闭模型，且全球南方研究被边缘化。
AI中文摘要：
尽管资金在结构上具有重要性，但人们对资金如何塑造科学中的人工智能参与方式知之甚少。我们分析了2018年至2025年间的104,226篇科学论文，将资金致谢与每篇论文参与基础模型的方式联系起来：是扩展模型（微调或在其基础上构建）、不加修改地使用模型，还是仅外围性地引用模型。研究得出三个发现。首先，资金来源与参与特征相关：公共资金与更高的开放权重模型参与度相关；仅私人资金选择性地促成模型扩展，且与开放性无稳健关联；混合资金在七个最大学科中的四个学科中名义上具有最高的开放权重模型参与率。其次，致谢行业云积分的论文使用开放权重模型的可能性较低，这与积分计划将研究引向封闭模型的现象一致。第三，行业合作具有独立的扩展溢价，这与企业内部资源通过合著渠道流动、而基于致谢的衡量方法无法捕捉到的情况相符。这种关联集中在计算机科学和全球北方合作中；全球南方研究在很大程度上被排除在外。随着公共资金收缩和行业计算供应扩大，科学人工智能工作正转向封闭的专有基础设施。
英文摘要：
How funding shapes AI engagement in science is poorly understood despite its structural importance. We analyze 104,226 papers (2018-2025), linking funding acknowledgments to how each paper engages with foundation models: whether it extends a model (fine-tunes or builds on it), uses one without modification, or references models only peripherally. We report three findings. First, both private and public funding are associated with greater AI engagement. Public funding is associated with more off-the-shelf and open-weight use, while private funding is associated with greater model extension. Industry cloud credits are associated with less open-weight use, consistent with greater reliance on closed models. Second, industry involvement is associated with model extension, suggesting that corporate resources reach science through coauthorship, though this channel is concentrated in Computer Science and Global North collaborations. Third, the gap across research systems is not one of AI adoption but of control. Off-the-shelf use is rising across fields and country groups, and the least publicly funded group, the Global South, now has the highest share of API-only engagement (0.4% to 10.0% of engaged papers). China shows the opposite profile: the highest public-funding share and lowest API-only engagement, with extension remaining stable as it declines across the Global North. Standard capacity indicators show convergence in AI adoption even as dependence on proprietary infrastructure diverges.
