Central Wikipedia articles are primarily edited by domain specialists while boundary-spanning articles are authored by generalists¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2018 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In the reported follow-up analysis, high-centrality articles were primarily edited by specialists active in only one domain, whereas high-betweenness boundary-spanning articles were primarily authored by generalists contributing to both domains. → Peter Holtz & Joachim Kimmerle & Ulrike Cress 2018

Evidence¶

Peter Holtz & Joachim Kimmerle & Ulrike Cress 2018¶

Peter Holtz & Joachim Kimmerle & Ulrike Cress. (2018). Using big data techniques for measuring productive friction in mass collaboration online environments. Intern. J. Comput.-Support. Collab. Learn. https://doi.org/10.1007/s11412-018-9285-y

q2 · i? · review · r2

The review reports this from further SNA analysis (Halatchliyski et al. 2014) of contributors' previous edits in the psychology and education domains; both article types were authored by more experienced users. No effect size is printed.

"In comparison, articles with high betweenness scores, which spanned the two domains, were primarily authored by generalists who regularly contributed to both domains."

Discussion¶

Related Claims¶

• New Wikipedia knowledge emerges around pivotal articles: high-centrality articles within domains and high-betweenness boundary-spanning articles between domains — related
• Artifacts and their interconnected structure operationalize collective knowledge processes in large-scale Wikipedia analysis — a broader claim this one bears on
• Three big data methods—text classification, cluster analysis, and social network analysis—can each yield insights into learning in mass collaboration — a broader claim this one bears on
• Boundary spanning perceptions and actions are critical mechanisms for productive divergent collaboration
• Wikipedia contributors show confirmation bias by preferentially editing articles consistent with their own views — related