Provenance record

Endorsement Without New Evidence: How Sequential Voting Inflates Mandates in Online Community Governance

arXiv cs.CY (Computers and Society) (tier 1, academic) 2026-09-10T04:00:00.000Z Original ↗

Source note: Preprints on societal impact.


Discourse valence
-50
Adverse
confidence 80% · 2 items
Adverse readingFavourable reading
Single-model reading. This item did not meet the threshold for a multi-model panel.
Evaluation and measurementGovernance and regulationConcentration of power
Excerpt as ingested

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

ModelProviderStageScoreConf.LatencyPromptWhen
Llama 3.3 70BMetaanalysis -50 80%3683ms v1.0.0 / m1.0.0 2026-09-10 09:04
Llama 3.3 70BMetaanalysis -20 70%3246ms v1.0.0 / m1.0.1 2026-09-10 09:17
Llama 3.3 70B · reading

Online voting systems may inflate mandates

evidence: speculative horizon: n/a
Llama 3.3 70B · reading

Study finds voting margins may overstate consensus

evidence: speculative horizon: n/a

Evidence extracted

The chain
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%