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200 00$a17th international symposium on open collaboration /$fGregorio Robles [and five others], editors
210  1$aNew York, New York :$cAssociation for Computing Machinery,$d2021.
215   $a1 online resource
311   $a1-4503-8500-1 
330   $aToday, digital platforms are increasingly mediating our day-to-day work and crowdsourced forms of labour are progressively gaining importance (e.g. Amazon Mechanical Turk, Universal Human Relevance System, TaskRabbit). In many popular cases of crowdsourcing, a volatile, diverse, and globally distributed crowd of workers compete among themselves to find their next paid task. The logic behind the allocation of these tasks typically operates on a "First-Come, First-Served" basis. This logic generates a competitive dynamic in which workers are constantly forced to check for new tasks. This article draws on findings from ongoing collaborative research in which we co-design, with crowdsourcing workers, three alternative models of task allocation beyond "First-Come, FirstServed", namely (1) round-robin, (2) reputation-based, and (3) contentbased. We argue that these models could create fairer and more collaborative forms of crowd labour. We draw on Amara On Demand, a remuneration-based crowdsourcing platform for video subtitling and translation, as the case study for this research. Using a multi-modal qualitative approach that combines data from 10 months of participant observation, 25 semi-structured interviews, two focus groups, and documentary analysis, we observed and co-designed alternative forms of task allocation in Amara on Demand. The identified models help envision alternatives towards more worker-centric crowdsourcing platforms, understanding that platforms depend on their workers, and thus ultimately they should hold power within them. 
606   $aOpen learning$vCongresses
615  0$aOpen learning
676   $a371.35
702   $aRobles$b Gregorio 
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996   $a17th International Symposium on Open Collaboration$92492316
997   $aUNINA