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1. The Challenges of Crowd Workers in Rural and Urban America

   Saviaga, C ; Li, Y ; Hanrahan, B ; Bigham, J ; Savage, S. ( January 2020 , Human computation)

   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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2. High royal jelly production does not impact the gut microbiome of honey bees

   https://doi.org/10.1186/s42523-021-00124-1  

   Damico, Megan E. ; Rueppell, Olav ; Shaffer, Zack ; Han, Bin ; Raymann, Kasie ( September 2021 , Animal Microbiome)

   Honey bees are not only essential for pollination services, but are also economically important as a source of hive products (e.g., honey, royal jelly, pollen, wax, and propolis) that are used as foods, cosmetics, and alternative medicines. Royal jelly is a popular honey bee product with multiple potential medicinal properties. To boost royal jelly production, a long-term genetic selection program of Italian honey bees (ITBs) in China has been performed, resulting in honey bee stocks (here referred to as RJBs) that produce an order of magnitude more royal jelly than ITBs. Although multiple studies have investigated the molecular basis of increased royal jelly yields, one factor that has not been considered is the role of honey bee-associated gut microbes.

   Based on the behavioral, morphological, physiological, and neurological differences between RJBs and ITBs, we predicted that the gut microbiome composition of RJBs bees would differ from ITBs. To test this hypothesis, we investigated the bacterial composition of RJB and ITB workers from an urban location and RJBs from a rural location in China. Based on 16S rRNA gene profiling, we did not find any evidence that RJBs possess a unique bacterial gut community when compared to ITBs. However, we observed differences between honey bees from the urban versus rural sites.

   Our results suggest that the environmental factors rather than stock differences are more important in shaping the bacterial composition in honey bee guts. Further studies are needed to investigate if the observed differences in relative abundance of taxa between the urban and rural bees correspond to distinct functional capabilities that impact honey bee health. Because the lifestyle, diet, and other environmental variables are different in rural and urban areas, controlled studies are needed to determine which of these factors are responsible for the observed differences in gut bacterial composition between urban and rural honeybees.

    

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3. Progression of COVID‐19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates

   https://doi.org/10.1111/jrh.12486  

   Paul, Rajib ; Arif, Ahmed A. ; Adeyemi, Oluwaseun ; Ghosh, Subhanwita ; Han, Dan ( June 2020 , The Journal of Rural Health)

   There are growing signs that the COVID‐19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county‐level change in prevalence rates of COVID‐19 by rural‐urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates.

   We used crowdsourced data on COVID‐19 and linked them to county‐level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R‐studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis‐Ord local statistics.

   In the rural counties, the mean prevalence of COVID‐19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID‐19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID‐19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25‐49 years had increased COVID‐19 prevalence rates.

   Our findings show a rapid spread of COVID‐19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID‐19 prevalence.

    

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4. Predicting the Working Time of Microtasks Based on Workers' Perception of Prediction Errors

   https://doi.org/10.15346/hc.v6i1.10  

   Susumu Saito, Chun-Wei Chiang ( October 2019 , Human computation)

   Crowd workers struggle to earn adequate wages. Given the limited task-related information provided on crowd platforms, workers often fail to estimate how long it would take to complete certain microtasks. Although there exist a few third-party tools and online communities that provide estimates of working times, such information is limited to microtasks that have been previously completed by other workers, and such tasks are usually booked immediately by experienced workers. This paper presents a computational technique for predicting microtask working times (i.e., how much time it takes to complete microtasks) based on past experiences of workers regarding similar tasks. The following two challenges were addressed during development of the proposed predictive model — (i) collection of sufficient training data labeled with accurate working times, and (ii) evaluation and optimization of the prediction model. The paper first describes how 7,303 microtask submission data records were collected using a web browser extension — installed by 83 Amazon Mechanical Turk (AMT) workers — created for characterization of the diversity of worker behavior to facilitate accurate recording of working times. Next, challenges encountered in defining evaluation and/or objective functions have been described based on the tolerance demonstrated by workers with regard to prediction errors. To this end, surveys were conducted in AMT asking workers how they felt regarding prediction errors in working times pertaining to microtasks simulated using an “imaginary” AI system. Based on 91,060 survey responses submitted by 875 workers, objective/evaluation functions were derived for use in the prediction model to reflect whether or not the calculated prediction errors would be tolerated by workers. Evaluation results based on worker perceptions of prediction errors revealed that the proposed model was capable of predicting worker-tolerable working times in 73.6% of all tested microtask cases. Further, the derived objective function contributed to realization of accurate predictions across microtasks with more diverse durations. 

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5. Towards fair and pro-social employment of digital pieceworkers for sourcing machine learning training data

   https://doi.org/10.1145/3491101.3516384  

   Rothschild, Annabel ; Booker, Justin ; Davoll, Christa ; Hill, Jessica ; Ivey, Venise and ; Rydal Shapiro, Ben ; DiSalvo, Betsy ( January 2022 , CHI Conference on Human Factors in Computing Systems Extended Abstracts)

   This work contributes to just and pro-social treatment of digital pieceworkers ("crowd collaborators") by reforming the handling of crowd-sourced labor in academic venues. With the rise in automation, crowd collaborators' treatment requires special consideration, as the system often dehumanizes crowd collaborators as components of the “crowd” [41]. Building off efforts to (proxy-)unionize crowd workers and facilitate employment protections on digital piecework platforms, we focus on employers: academic requesters sourcing machine learning (ML) training data. We propose a cover sheet to accompany submission of work that engages crowd collaborators for sourcing (or labeling) ML training data. The guidelines are based on existing calls from worker organizations (e.g., Dynamo [28]); professional data workers in an alternative digital piecework organization; and lived experience as requesters and workers on digital piecework platforms. We seek feedback on the cover sheet from the ACM community 

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