Endorsement Without New Evidence: How Sequential Voting Inflates Mandates in Online Community Governance
Source note: Preprints on societal impact.
arXiv:2609.09321v1 Announce Type: cross Abstract: 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.
Every model that read this
| Model | Provider | Stage | Score | Conf. | Latency | Prompt | When |
|---|---|---|---|---|---|---|---|
| Llama 3.3 70B | Meta | analysis | -50 | 80% | 3683ms | v1.0.0 / m1.0.0 | 2026-09-10 09:04 |
| Llama 3.3 70B | Meta | analysis | -20 | 70% | 3246ms | v1.0.0 / m1.0.1 | 2026-09-10 09:17 |
Online voting systems may inflate mandates
Study finds voting margins may overstate consensus
Evidence extracted
- Large support margins in online elections can overstate independent scrutiny
- Vote-text divergence rises as voters arrive later
- Vote-text divergence rises as voters arrive later
- Divergence does not predict worse post-promotion outcomes
SOURCE arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT 4651395d-9722-4548-9fc8-0dc30f25fbf1
https://arxiv.org/abs/2609.09321
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EVIDENCE 4 extracted excerpts
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MODEL RUN 2 runs, methodology 1.0.1
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SCORE -50 (Adverse)
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CONFIDENCE 80%