Learning patterns/Engaging participants through critical theory
What problem does this solve? edit
Wikipedia has extensively documented biases in its coverage, as documented in media and by projects like WikiProject Countering Systemic Bias. Knowing this, people have planned events around facing this issue head-on and then working on improving Wikipedia to remediate this issue.
What is the solution? edit
A critical Wikipedia edit-a-thon, targeted toward an academic audience, uses the lens of critical theory to address issues of systemic bias on Wikipedia. From Wikipedia: "Critical theory is a school of thought that stresses the reflective assessment and critique of society and culture by applying knowledge from the social sciences and the humanities." The edit-a-thon begins by highlighting the issues of systemic bias present in Wikipedia, noting how certain points of view are absent as a result of the circumstances under which Wikipedia operates (including that it is a volunteer project, meaning you need to have the time and resources to volunteer, etc.). This can be followed up with case studies on how these biases are propagated through other means, as well as efforts to help remediate them. These introductory presentations set the tone of the event, which focuses on improving articles on topics traditionally left out.
Things to consider edit
- To maximize participation at this event, provide child care as an option for participants
- Not everyone will agree. This is fine.
- Establish ground rules (e.g., safe space policies) at the beginning of the event.
- Invite local researchers and/or students to present relevant work.
When to use edit
See also edit
Related patterns edit
- The most successful editathons, including art and feminism among many others, have focused on something that activates a set of values, based on a larger critique of something (whether its coverage of women, race, gaps in public history, engagement in the humanities, etc.) Sadads (talk) 14:18, 30 March 2015 (UTC)
- I think this type of approach is especially effective in academic environments where there's an existing, ongoing dialogue about institutional and/or systemic bias.--Mssemantics (talk) 03:31, 2 April 2015 (UTC)