Research:Revision scoring as a service/Stories

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This page documents stories written by people who found the project to be useful for them.

Updating WikiProject Medicine article assessments[edit]

Recent work in article quality assessment detection[1][2] can enable us to automatically identify which articles are most due to be re-assessed. Let's apply this method to WikiProject Medicine's stubs.

Discussion: Research:Screening WikiProject Medicine articles for quality

Andrew Lih experiments with ORES in the classroom[edit]

[...] having students excited and experiencing that instant dopamine hit when editing articles is a breath of fresh air when pleasurable interactions seem to be less frequent today than in Wikipedia’s early days.

Discussion: en:User:Fuzheado/ORES experiment

Smithsonian American Art Museum edit-a-thon[edit]

Our hand-assessed article ratings are looking quite stale. You may be skeptical of whether a machine learning algorithm can do a decent job of properly assessing articles. However, in just two weeks of experimenting with ORES, it seems to be far more preferable than putting stock in stub/start/C human-assessed ratings that may be months or years old. --Andrew Lih

Discussion: https://www.facebook.com/groups/glamwikius/permalink/579619518862783/ Results: en:Wikipedia:Meetup/DC/SAAM AU 2016#Harlem Renaissance artists

See also[edit]

  • RA-UN, which uses Revscore to identify vandalism [1]
  • WikiProject Cannabis (English Wikipedia)

References[edit]

  1. Warncke-Wang, M., Cosley, D., & Riedl, J. (2013, August). Tell me more: an actionable quality model for Wikipedia. In Proceedings of the 9th International Symposium on Open Collaboration (p. 8). ACM.
  2. Halfaker, A. & Warncke-Wang, M., Wiki-Class: Wikipedia article quality classification. docs - repo