Difference between revisions of "Work-To-Rule: the Emergence of Algorithmic Governance in Wikipedia"
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| − | '''Work-To-Rule: the Emergence of Algorithmic Governance in Wikipedia''' - scientific work related to Wikipedia quality published in 2013, written by Claudia Müller-Birn, Leonhard Dobusch and James D. Herbsleb. | + | '''Work-To-Rule: the Emergence of Algorithmic Governance in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Claudia Müller-Birn]], [[Leonhard Dobusch]] and [[James D. Herbsleb]]. |
== Overview == | == Overview == | ||
| − | Research has shown the importance of a functioning governance system for the success of peer production communities. It particularly highlights the role of human coordination and communication within the governance regime. In this article, authors extend this line of research by differentiating two categories of governance mechanisms. The first category is based primarily on communication, in which social norms emerge that are often formalized by written rules and guidelines. The second category refers to the technical infrastructure that enables users to access artifacts, and that allows the community to communicate and coordinate their collective actions to create those artifacts. Authors collected qualitative and quantitative data from Wikipedia in order to show how a community's consensus gradually converts social mechanisms into algorithmic mechanisms. In detail, authors analyze algorithmic governance mechanisms in two embedded cases: the software extension "flagged revisions" and the bot "xqbot". Authors insights point towards a growing relevance of algorithmic governance in the realm of governing large-scale peer production communities. This extends previous research, in which algorithmic governance is almost absent. Further research is needed to unfold, understand, and also modify existing interdependencies between social and algorithmic governance mechanisms. | + | Research has shown the importance of a functioning governance system for the success of peer production communities. It particularly highlights the role of human coordination and communication within the governance regime. In this article, authors extend this line of research by differentiating two [[categories]] of governance mechanisms. The first category is based primarily on communication, in which social norms emerge that are often formalized by written rules and guidelines. The second category refers to the technical infrastructure that enables users to access artifacts, and that allows the community to communicate and coordinate their collective actions to create those artifacts. Authors collected qualitative and quantitative data from [[Wikipedia]] in order to show how a community's consensus gradually converts social mechanisms into algorithmic mechanisms. In detail, authors analyze algorithmic governance mechanisms in two embedded cases: the software extension "flagged revisions" and the bot "xqbot". Authors insights point towards a growing relevance of algorithmic governance in the realm of governing large-scale peer production communities. This extends previous research, in which algorithmic governance is almost absent. Further research is needed to unfold, understand, and also modify existing interdependencies between social and algorithmic governance mechanisms. |
Revision as of 08:33, 20 May 2020
Work-To-Rule: the Emergence of Algorithmic Governance in Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Claudia Müller-Birn, Leonhard Dobusch and James D. Herbsleb.
Overview
Research has shown the importance of a functioning governance system for the success of peer production communities. It particularly highlights the role of human coordination and communication within the governance regime. In this article, authors extend this line of research by differentiating two categories of governance mechanisms. The first category is based primarily on communication, in which social norms emerge that are often formalized by written rules and guidelines. The second category refers to the technical infrastructure that enables users to access artifacts, and that allows the community to communicate and coordinate their collective actions to create those artifacts. Authors collected qualitative and quantitative data from Wikipedia in order to show how a community's consensus gradually converts social mechanisms into algorithmic mechanisms. In detail, authors analyze algorithmic governance mechanisms in two embedded cases: the software extension "flagged revisions" and the bot "xqbot". Authors insights point towards a growing relevance of algorithmic governance in the realm of governing large-scale peer production communities. This extends previous research, in which algorithmic governance is almost absent. Further research is needed to unfold, understand, and also modify existing interdependencies between social and algorithmic governance mechanisms.
