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  1. null (Ed.)
    Crowdworkers depend on Amazon Mechanical Turk (AMT) as an important source of income and it is left to workers to determine which tasks on AMT are fair and worth completing. While there are existing tools that assist workers in making these decisions, workers still spend significant amounts of time finding fair labor. Difficulties in this process may be a contributing factor in the imbalance between the median hourly earnings ($2.00/hour) and what the average requester pays ($11.00/hour). In this paper, we study how novices and experts select what tasks are worth doing. We argue that differences between the two populations likely lead to the wage imbalances. For this purpose, we first look at workers' comments in TurkOpticon (a tool where workers share their experience with requesters on AMT). We use this study to start to unravel what fair labor means for workers. In particular, we identify the characteristics of labor that workers consider is of "good quality'' and labor that is of "poor quality'' (e.g., work that pays too little.) Armed with this knowledge, we then conduct an experiment to study how experts and novices rate tasks that are of both good and poor quality. Through our research we uncover that experts and novices both treat good quality labor in the same way. However, there are significant differences in how experts and novices rate poor quality labor, and whether they believe the poor quality labor is worth doing. This points to several future directions, including machine learning models that support workers in detecting poor quality labor, and paths for educating novice workers on how to make better labor decisions on AMT. 
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  2. null (Ed.)
    Crowd work has the potential of helping the financial recovery of regions traditionally plagued by a lack of economic opportunities, e.g., rural areas. However, we currently have limited information about the challenges facing crowd workers from rural and super rural areas as they struggle to make a living through crowd work sites. This paper examines the challenges and advantages of rural and super rural Amazon Mechanical Turk (MTurk) crowd workers and contrasts them with those of workers from urban areas. Based on a survey of 421 crowd workers from differing geographic regions in the U.S., we identified how across regions, people struggled with being onboarded into crowd work. We uncovered that despite the inequalities and barriers, rural workers tended to be striving more in micro-tasking than their urban counterparts. We also identified cultural traits, relating to time dimension and individualism, that offer us an insight into crowd workers and the necessary qualities for them to succeed on gig platforms. We finish by providing design implications based on our findings to create more inclusive crowd work platforms and tools. 
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  3. null (Ed.)
    Crowd work has the potential of helping the financial recovery of regions traditionally plagued by a lack of economic opportunities, e.g., rural areas. However, we currently have limited information about the challenges facing crowd workers from rural and super rural areas as they struggle to make a living through crowd work sites. This paper examines the challenges and advantages of rural and super rural Amazon Mechanical Turk (MTurk) crowd workers and contrasts them with those of workers from urban areas. Based on a survey of 421 crowd workers from differing geographic regions in the U.S., we identified how across regions, people struggled with being onboarded into crowd work. We uncovered that despite the inequalities and barriers, rural workers tended to be striving more in micro-tasking than their urban counterparts. We also identified cultural traits, relating to time dimension and individualism, that offer us an insight into crowd workers and the necessary qualities for them to succeed on gig platforms. We finish by providing design implications based on our findings to create more inclusive crowd work platforms and tools. 
    more » « less