规模化可重复性：一种用于大规模复制与再分析的人工智能辅助工作流
Scaling Reproducibility: An AI-Assisted Workflow for Large-Scale Replication and Reanalysis
AI总结：
提出一种AI辅助工作流，实现论文全自动复制，并应用于政治学顶级期刊的384篇论文，发现期刊验证要求与数据存档政策显著提升可重复性。
摘要：
计算可重复性是科学可信度的核心，然而大规模验证已发表成果的成本仍然高昂。我们开发了一种AI辅助工作流，用于自动化的全文复制——包括检索材料、重建环境、执行代码，并将输出与回归表中报告的点估计进行匹配。我们定义了三大政治学顶级期刊（2010-2025年）所有实证与定量论文的总体，并通过自动提取衡量声明的数据可用性。对于384项研究的分层样本，我们应用该工作流进行全文复制，共计3,523个实证模型。我们发现，期刊验证要求与数据存档规定共同推动了可重复性：完全或大部分可复制的论文比例从DA-RT采纳前的20.8%上升至采纳后的82.5%，并且在可获取复制包的条件下，92.1%的论文完全或大部分可复制（234/254）。作为二次应用，我们对84项研究（在1,910个复制模型中的597个IV设定）应用标准化IV诊断，展示了自动执行如何实现跨异质经验情境的系统性再分析。
英文摘要：
Computational reproducibility is central to scientific credibility, yet verifying published results at scale remains costly. We develop an AI-assisted workflow for automated full-paper replication -- retrieving materials, reconstructing environments, executing code, and matching outputs to point estimates reported in regression tables. We define a universe of all empirical and quantitative papers from the three top political science journals (2010--2025) and measure stated data availability using automated extraction. For a stratified sample of 384 studies, we apply the workflow to conduct full-paper replication, totaling 3,523 empirical models. We find that journal verification requirements, combined with data archiving mandates, drive reproducibility: the share of fully or largely reproducible papers rises from 20.8% before DA-RT adoption to 82.5% after, and conditional on accessible replication packages, 92.1% of papers are fully or largely reproducible (234/254). As a secondary application, we apply standardized IV diagnostics to 84 studies (597 IV specifications among 1,910 replicated models), illustrating how automated execution enables systematic reanalysis across heterogeneous empirical settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
