发表机构
Stevens Institute of Technology; University of Massachusetts Boston（史蒂文斯理工学院; 马萨诸塞大学波士顿分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过维基百科管理员选举数据提出投票文本分歧度，发现顺序投票中的薄弱理由会夸大支持率，但未必影响晋升后表现。
AI 中文摘要
在线社区通常将公开选举中的大比例支持视为强有力的授权。我们认为，这种比例可能高估了决策背后的独立审查程度。利用维基百科管理员选举中的198,275条自由文本理由，我们引入了投票文本分歧度这一度量，用于标记决定性投票与薄弱、顺从性理由配对的情况。即使在控制投票者和选举固定效应后，分歧度随投票者到达时间的推迟而上升。这一模式与信息饱和一致：一旦考虑先前文本，到达顺序不再预测分歧度，而累积的先前证据则能预测。该效应在共同投票网络中的边缘投票者中最为显著。然而，分歧度并不能预测晋升后更差的结果，如管理活动或存续情况。因此，公开的票数统计可能削弱审查信号，即使同时能选出有能力的管理员：一个比例可能显得比实际包含更多的共识和支持。
英文摘要
Online communities often treat large support margins in public elections as strong mandates. We argue that such margins can overstate the independent scrutiny behind a decision. Using 198,275 free-text rationales from Wikipedia admin elections, we introduce vote-text divergence, a measure that flags a decisive vote paired with a thin, deferential rationale. Divergence rises as voters arrive later, even after controlling for voter and election fixed effects. The pattern is consistent with information saturation: once prior text is accounted for, arrival order no longer predicts divergence, while accumulated prior evidence does. The effect is strongest among peripheral voters in the co-voting network. Yet divergence does not predict worse post-promotion outcomes, such as administrative activity or survival. Public tallies can therefore weaken the scrutiny signal even while selecting capable administrators: a margin may appear to reflect more consensus and support than it actually contains.
Comments11 pages, 2 figures, 5 tables. Under review
