发表机构
Olin Business School, Washington University in St. Louis（华盛顿大学圣路易斯分校奥林商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究以Kaggle平台数据为样本，发现AI时代证书仍具信号价值，仅上传竞赛奖牌存量信息性大幅下降，且提出基于AI前数据构建的加权指数能更优预测AI时代表现。
AI 中文摘要
生成式AI如今可完成众多认证机构用以评估技能的任务。在AI时代，证书是否仍保留着对后续表现的信号价值？答案基本是肯定的。我们审计了最大数据科学竞赛平台Kaggle2010-2026年的档案，该平台同时运行两种评估形式：上传竞赛（upload-competitions），直接对参赛者基于公开数据计算的预测进行评分；代码竞赛（code-competitions），通过在隐藏数据上执行参赛者的代码来对预测评分。在444698次参与中，竞赛奖牌在获得后的第一年里，几乎完全能预测两种形式下后续的排行榜表现。新奖牌在AI转型期间保留了大部分信号价值；证书存量仅与它们的补充情况一样具有信息性。尽管上传竞赛的奖牌存量失去了82%的信息性，但机构滞留解释了损失的一半到四分之三：上传竞赛因早于AI的原因已退出，其冻结的奖牌存量在原有衰减模式下老化。旧上传竞赛奖牌仅在单独使用时看起来更有价值，因为它们能代表持有者记录的其余部分（如经验）。观测到的变化是机构层面而非个人层面的：类AI的工作风格在两种形式中对表现的预测效果相似。平台基于终身奖牌数的官方证书等级，丢弃了13-16%的奖牌信息；仅基于AI前时代数据构建的、对近期奖牌赋予更高权重的指数，在预测AI时代表现时优于官方等级。综上，证书具有信息性、易逝性、机构绑定性和相互依赖性；在AI下维持其价值是一个关乎制度设计的高风险社会经济问题。
英文摘要
Platforms summarize providers' past achievements into credentials that buyers use to judge quality. Generative AI can now produce much of the work those achievements certify, raising fears that those quality signals are worthless. We audit how well such credentials stay informative in Kaggle's 2010-2026 archive, where medals are won on predictions scored against withheld answers and two evaluation formats ran side by side. Across 444,698 participations, a medal's power to predict performance sits almost entirely in its first year, in both formats and eras. Fresh medals kept most of their value through the AI transition. About half of the collapse in the informativeness of one format's medals is institutional: the platform had been phasing out that format before AI, and its medal stock aged out on schedule. Old medals look more informative only in isolation. The platform's official lifetime-tier display discards up to a sixth of the medals' predictive power. A recency-weighted index fit before the AI era explains AI-era performance about 13% better than the tiers and selects entrants who perform better on average, though the tiers still identify extreme top performers better. Displaying a recent-performance summary alongside the lifetime tiers would recover the discarded information for buyers.
Comments22 pages including appendix, 6 figures. Revised version, September 2026: new title, points-ranking benchmark, screening exercise, attribution stated as the identified component. Data and code: https://doi.org/10.17605/OSF.IO/GQW9B
