00897nam2-22002891i-450-990000226830403321000022683FED01000022683(Aleph)000022683FED0100002268320011111d--------km-y0itay50------baitay-------001yy2.: Trigonometrie. Analytische geometrieder ebene und raums differential- undintegralrechnung mechanik festigkeitslehrebaustatik / bearbeitet von G. Ehrig , G.Chr. Mehrtens. XXIV, 770 p. : ill.LeipzigW. Engelmann1920-v.27 cm0010000113612001Lehrbuch der mathematikMatematica510EsselbornITUNINARICAUNIMARCBK99000022683040332113 K 05 0721934FINBCFINBCUNINAING0102572nam 2200337 450 991051042240332120230825115914.0(CKB)4930000000238599(NjHacI)994930000000238599(EXLCZ)99493000000023859920230825d2021 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrier17th international symposium on open collaboration /Gregorio Robles [and five others], editorsNew York, New York :Association for Computing Machinery,2021.1 online resource1-4503-8500-1 Today, 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. Open learningCongressesOpen learning371.35Robles Gregorio NjHacINjHaclBOOK991051042240332117th International Symposium on Open Collaboration2492316UNINA