Crowdsourcing: the Wiki Way of Working

Crowdsourcing: the Wiki Way of Working explains an approach to organising work that is in some ways the opposite of traditional planning and management. It draws out some lessons from the most visibly successful crowdsourcing projects that support education and research, including Wikipedia. It shows how these community-based projects, despite their unorthodox methods, share educational and scholarly objectives with more traditional institutions and projects. It suggests ways in which those institutions and projects can benefit from working with Wikipedia and the wider Wikimedia community.

It was originally published in February 2014 as an infoKit on the Jisc infoNet site and is reproduced here under its CC-BY-SA licence. It can be read as a series of self-contained points or case-studies, or as a journey from theory to practice. Development of the infoKit was funded by Jisc and Wikimedia UK. The principal author of the text is Martin Poulter. Improvements are welcome to this Wikibook edition of the infoKit.

This is not meant to be a comprehensive manual of crowdsourcing. The topic is also addressed in parts of the Wikibooks Citizen Science and Lentis: The Social Interface of Technology.

Contents

1. Two approaches to complex tasks
2. The Wikipedia way
   1. Division of labour
   2. Wikipedia, the triumph of crowdsourcing
   3. Progress without a plan
   4. The drive for quality
   5. Summing up
3. Free content and open processes
   1. Making soup with stone
   2. Intellectual property
   3. Network effects (the power of one)
   4. Summing up
4. Community design
   1. Norms and culture
   2. A shared goal
   3. The right kind of goal
   4. Summing up
5. Gamification
   1. Managing motivations
   2. Recognition and badging in Wikipedia
   3. Summing up
6. Crowdsourcing in practice
   1. Division of labour for a scholarly database
   2. Defining progress: geographical data
   3. Motivation: documenting a town
7. Crowdsourcing the restoration and reuse of images
   1. Keeping everybody happy
   2. Image restoration
   3. Contextualisation
   4. Improving image metadata
   5. Summing up
8. Further reading

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