crowd in the cloud: collaborative frameworks for virtual dh projects
DESCRIPTION
Digital History workshop: Crowdsourcing in the Humanities and cultural heritage sector. Victoria University of Wellington 23 April 2013 Session: Crowd in the Cloud: Collaborative Frameworks for Virtual DH Projects Presenter: Lynne Siemens http://wtap.vuw.ac.nz/wordpress/digital-history/events/crowdsourcing-workshop/presenters/TRANSCRIPT
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Crowd in the Cloud: Collaborative Frameworks for Virtual DH Projects Lynne Siemens [email protected] Wellington, April 2013
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A glorious endeavour….
h"p://collec*on.cooperhewi".org/objects/18422089/, Drawing, "Group of Angels on a Cloud Bank (study for a ceiling decora*on)", 1630–50, Smithsonian Cooper-‐Hewi", Na*onal Design Museum
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Perhaps, more the reality…
Devils, from the Last Judgement, Luca Signorelli, hBp://www.1st-‐art-‐gallery.com/Luca-‐Signorelli/Devils,-‐From-‐The-‐Last-‐Judgement.html
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Appropriate middle ground? • Crowdsourcing offers potenKal to academic projects, especially for those with large amounts of data to process and relaKvely small budgets
• But how best to organize the work to ensure that this crowd’s contribuKon is delivered within an academic project’s schedule, budget and other resources and to the required quality standard? • ConsideraKon of more than the moKvaKon of volunteers
• Where are the points of collaboraKon?
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Collaboration Points
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Decision points for collaboration in the cloud • These include: • the type of experKse, qualificaKon and/or knowledge required • the presence of contributors • the mechanisms by which they will parKcipate and contribute • project remuneraKon • moKvators to keep parKcipants engaged • quality control mechanisms
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Frameworks: Where/how/what/who/when to collaborate? Simple tasks Moderate tasks Complex tasks
• Task: Low complexity • Outcomes and
quality: Easy to evaluate
• “Any Individual” could undertake with minimal training, skill, and special experKse
• PotenKal for gamificaKon
• Example: OCR correcKon and tagging
• Task: Medium complexity
• Outcomes and quality: More difficult to evaluate
• “Most people” could undertake with training, skill, and special experKse
• Example: TranscripKon
• Task: High complexity
• Outcomes and quality: Difficult to evaluate
• “Expert” needed with special knowledge and skills
• Example: AnnotaKon and problem solving
Australian Historic Newspapers
Transcribe Bentham Pynchonwiki
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Questions/discussion