The COVID-19 pandemic led to the confinement of populations in France on the one hand and to shortages of equipment on the other hand (in particular Personal Protective Equipment). The makers therefore mobilized worldwide to produce this medical equipment. In the Hauts-de-France region, a group of makers organized to produce face shields for hospitals, public health and social care institutions and also for retailers. Our analysis of the collaborative messaging room used to coordinate the production of face shields was completed by the interview of active makers. It was based on an original tool-based integrated and hybrid (quantitative/qualitative) methodology. That work enabled us to update the profile of the participants, the intensity of their contribution, the nature of the innovation implemented, the coordination mechanisms, the associated difficulties and the role of technologies in the makers' response.
{"title":"How makers responded to the PPE shortage during the COVID-19 pandemic: an analysis focused on the Hauts-de-France region","authors":"R. Viseur, Berengere Fally, Amel Charleux","doi":"10.1145/3479986.3479989","DOIUrl":"https://doi.org/10.1145/3479986.3479989","url":null,"abstract":"The COVID-19 pandemic led to the confinement of populations in France on the one hand and to shortages of equipment on the other hand (in particular Personal Protective Equipment). The makers therefore mobilized worldwide to produce this medical equipment. In the Hauts-de-France region, a group of makers organized to produce face shields for hospitals, public health and social care institutions and also for retailers. Our analysis of the collaborative messaging room used to coordinate the production of face shields was completed by the interview of active makers. It was based on an original tool-based integrated and hybrid (quantitative/qualitative) methodology. That work enabled us to update the profile of the participants, the intensity of their contribution, the nature of the innovation implemented, the coordination mechanisms, the associated difficulties and the role of technologies in the makers' response.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122026424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
COVID-19 has highlighted the importance of digital in the fight against the pandemic (control at the border, automated tracing, creation of databases...). In this research, we analyze the Belgian response in terms of open data. First, we examine the open data publication strategy in Belgium (a federal state with a sometimes complex functioning, especially in health), second, we conduct a case study (anatomy of the pandemic in Belgium) in order to better understand the strengths and weaknesses of the main COVID-19 open data repository. And third, we analyze the obstacles to open data publication. Finally, we discuss the Belgian COVID-19 open data strategy in terms of data availability, data relevance and knowledge management. In particular, we show how difficult it is to optimize the latter in order to make the best use of governmental, private and academic open data in a way that has a positive impact on public health policy.
{"title":"Open data in digital strategies against COVID-19: the case of Belgium","authors":"R. Viseur","doi":"10.1145/3479986.3479988","DOIUrl":"https://doi.org/10.1145/3479986.3479988","url":null,"abstract":"COVID-19 has highlighted the importance of digital in the fight against the pandemic (control at the border, automated tracing, creation of databases...). In this research, we analyze the Belgian response in terms of open data. First, we examine the open data publication strategy in Belgium (a federal state with a sometimes complex functioning, especially in health), second, we conduct a case study (anatomy of the pandemic in Belgium) in order to better understand the strengths and weaknesses of the main COVID-19 open data repository. And third, we analyze the obstacles to open data publication. Finally, we discuss the Belgian COVID-19 open data strategy in terms of data availability, data relevance and knowledge management. In particular, we show how difficult it is to optimize the latter in order to make the best use of governmental, private and academic open data in a way that has a positive impact on public health policy.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121694063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Much prior research has found gender bias in peer production systems like Wikipedia and OpenStreetMap. This bias affects both women’s participation in these platforms and content about women on these platforms. We investigated the gender content gap in Wikidata, where less than 22% of items that represent people are about women. We asked: what is the source of this bias? Specifically, does it originate from the actions of Wikidata editors or from external factors; that is, does it simply reflect existing real world gender bias? We conducted a quantitative case study that found: (i) the most popular categories of people included in Wikidata represent male-dominant professions, such as American football; (ii) within a selected set of professions where we could obtain gender distribution data, Wikidata is no more biased than the real world: men and women are included at similar percentages, and the quality of items representing men and women also is similar. We provide possible explanations for our findings and implications for addressing the Wikidata content gap.
{"title":"Quantifying the Gap: A Case Study of Wikidata Gender Disparities","authors":"C. Zhang, L. Terveen","doi":"10.1145/3479986.3479992","DOIUrl":"https://doi.org/10.1145/3479986.3479992","url":null,"abstract":"Much prior research has found gender bias in peer production systems like Wikipedia and OpenStreetMap. This bias affects both women’s participation in these platforms and content about women on these platforms. We investigated the gender content gap in Wikidata, where less than 22% of items that represent people are about women. We asked: what is the source of this bias? Specifically, does it originate from the actions of Wikidata editors or from external factors; that is, does it simply reflect existing real world gender bias? We conducted a quantitative case study that found: (i) the most popular categories of people included in Wikidata represent male-dominant professions, such as American football; (ii) within a selected set of professions where we could obtain gender distribution data, Wikidata is no more biased than the real world: men and women are included at similar percentages, and the quality of items representing men and women also is similar. We provide possible explanations for our findings and implications for addressing the Wikidata content gap.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129004965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pablo Cruz, Felipe Beroíza, Francisco Ponce, H. Astudillo
The Open Innovation paradigm has spread widely since 2003, and led to the emergence of Open Innovation Platforms as software systems aiming at supporting and facilitating open innovation initiatives and projects. This software domain has matured up to a point where many functional concepts became notably common and used in these platforms. When implementing open innovation platforms, related people often struggle when defining expected functional characteristics due to the general application of the paradigm, making necessary the existence of a model that provide a set of potential functional features expected in the creation and development of this type of platform. Reference models provides a domain-specific set of clearly defined entities aiming at encouraging better communication in the domain. We propose in this paper a reference model for capturing and defining the functional features that could be implemented in outside-in oriented open innovation platforms. For building this reference model, we reviewed some of the already published reports of open innovation platforms implementations in order determine and define the potential functional features expected in this kind of platforms. We believe this knowledge base could ease software development and deployment decisions, especially at early stages where open innovation platforms adopters face development in a domain that as of this writing is still new to many people.
{"title":"A Reference Model for Outside-in Open Innovation Platforms","authors":"Pablo Cruz, Felipe Beroíza, Francisco Ponce, H. Astudillo","doi":"10.1145/3479986.3479998","DOIUrl":"https://doi.org/10.1145/3479986.3479998","url":null,"abstract":"The Open Innovation paradigm has spread widely since 2003, and led to the emergence of Open Innovation Platforms as software systems aiming at supporting and facilitating open innovation initiatives and projects. This software domain has matured up to a point where many functional concepts became notably common and used in these platforms. When implementing open innovation platforms, related people often struggle when defining expected functional characteristics due to the general application of the paradigm, making necessary the existence of a model that provide a set of potential functional features expected in the creation and development of this type of platform. Reference models provides a domain-specific set of clearly defined entities aiming at encouraging better communication in the domain. We propose in this paper a reference model for capturing and defining the functional features that could be implemented in outside-in oriented open innovation platforms. For building this reference model, we reviewed some of the already published reports of open innovation platforms implementations in order determine and define the potential functional features expected in this kind of platforms. We believe this knowledge base could ease software development and deployment decisions, especially at early stages where open innovation platforms adopters face development in a domain that as of this writing is still new to many people.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121124153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neeru Dubey, Amit Arjun Verma, S. Iyengar, Simran Setia
The complex collaborative structure of Wikipedia has attracted researchers from various domains, such as social networks, human-computer interaction, and collective intelligence. Yet, a few focus on the readers’ perception of Wikipedia. Readers make up the majority of Wikipedia users (editors/readers), and being on the consumption side, readers play a crucial role in its sustenance. The attention patterns of users while reading an article can reveal users’ interest distribution as well as content quality of the article. In this paper, we present an Attention Feedback (AF) approach for Wikipedia readers. The fundamental idea of the proposed approach comprises the implicit capture of gaze-based feedback of Wikipedia readers using a commodity gaze tracker. The developed AF mechanism aims at overcoming the main limitation of the currently used “pageview” and “survey” based feedback approaches, i.e., data inaccuracy. Moreover, the incorporation of a single-camera image processing-based gaze tracker makes the overall system cost-efficient and portable. The proposed approach can be extended to enable the research community to analyze various online portals as well as offline documents from the readers’ perspective.
{"title":"Implicit Visual Attention Feedback System for Wikipedia Users","authors":"Neeru Dubey, Amit Arjun Verma, S. Iyengar, Simran Setia","doi":"10.1145/3479986.3479993","DOIUrl":"https://doi.org/10.1145/3479986.3479993","url":null,"abstract":"The complex collaborative structure of Wikipedia has attracted researchers from various domains, such as social networks, human-computer interaction, and collective intelligence. Yet, a few focus on the readers’ perception of Wikipedia. Readers make up the majority of Wikipedia users (editors/readers), and being on the consumption side, readers play a crucial role in its sustenance. The attention patterns of users while reading an article can reveal users’ interest distribution as well as content quality of the article. In this paper, we present an Attention Feedback (AF) approach for Wikipedia readers. The fundamental idea of the proposed approach comprises the implicit capture of gaze-based feedback of Wikipedia readers using a commodity gaze tracker. The developed AF mechanism aims at overcoming the main limitation of the currently used “pageview” and “survey” based feedback approaches, i.e., data inaccuracy. Moreover, the incorporation of a single-camera image processing-based gaze tracker makes the overall system cost-efficient and portable. The proposed approach can be extended to enable the research community to analyze various online portals as well as offline documents from the readers’ perspective.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115408352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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, First-Served”, namely (1) round-robin, (2) reputation-based, and (3) content-based. 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.
今天，数字平台越来越多地调解我们的日常工作，众包形式的劳动正逐渐变得越来越重要(例如亚马逊机械土耳其人，通用人类关联系统，TaskRabbit)。在许多流行的众包案例中，一群不稳定的、多样化的、分布在全球的工人相互竞争，寻找下一个有报酬的任务。这些任务分配背后的逻辑通常是基于“先到先得”的原则。这种逻辑产生了一种竞争动态，在这种动态中，工人们不断被迫检查新任务。本文借鉴了正在进行的合作研究的发现，我们与众包工作者共同设计了“先到先得”之外的三种替代任务分配模型，即(1)循环，(2)基于声誉和(3)基于内容。我们认为，这些模型可以创造更公平、更协作的群体劳动形式。我们将Amara on Demand(一个基于报酬的视频字幕和翻译众包平台)作为本研究的案例。采用多模态定性方法，结合了10个月的参与者观察、25个半结构化访谈、两个焦点小组和文献分析的数据，我们观察并共同设计了Amara on Demand中任务分配的替代形式。确定的模型有助于设想以工人为中心的众包平台的替代方案，理解平台依赖于他们的工人，因此最终他们应该在他们内部掌握权力。
{"title":"The platform belongs to those who work on it! Co-designing worker-centric task distribution models","authors":"David Rozas, Jorge Saldivar, Eve Zelickson","doi":"10.1145/3479986.3479987","DOIUrl":"https://doi.org/10.1145/3479986.3479987","url":null,"abstract":"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, First-Served”, namely (1) round-robin, (2) reputation-based, and (3) content-based. 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.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"60 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131609874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Compared to Wikipedia, Wikidata is a single domain website with the possibility to view information in multiple languages. Translation plays a significant role in Wikidata. Unlike Wikidata items, Wikidata properties are influenced less by translation bots and require a meaningful amount of human effort. The study of Wikidata property creation and translation is, therefore, very essential. Since the inception of Wikipedia, several research works have focused on the information flow among different language Wikipedias. The attention has now shifted to the way information on Wikidata is created and translated. The focus of this article is the Wikidata properties. WDProp is a web application created to understand and obtain an integrated view on the various multilingual aspects of Wikidata properties, from their proposition to their use on multiple domains.
{"title":"WDProp: Web Application to Analyse Multilingual Aspects of Wikidata Properties","authors":"John Samuel","doi":"10.1145/3479986.3479996","DOIUrl":"https://doi.org/10.1145/3479986.3479996","url":null,"abstract":"Compared to Wikipedia, Wikidata is a single domain website with the possibility to view information in multiple languages. Translation plays a significant role in Wikidata. Unlike Wikidata items, Wikidata properties are influenced less by translation bots and require a meaningful amount of human effort. The study of Wikidata property creation and translation is, therefore, very essential. Since the inception of Wikipedia, several research works have focused on the information flow among different language Wikipedias. The attention has now shifted to the way information on Wikidata is created and translated. The focus of this article is the Wikidata properties. WDProp is a web application created to understand and obtain an integrated view on the various multilingual aspects of Wikidata properties, from their proposition to their use on multiple domains.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115283253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As Wikipedia has grown in popularity, it is important to investigate its diverse user community and collaborative editorial base. Although all user data, from traffic to user edits, are available for download under a free and open license, it is difficult to work with this data due to its scale. In this paper, we demonstrate how consumer hardware can be used to create a local database of Wikipedia’s full edit history from their public XML data dumps. Using this database, we create and present the first visualizations of how editing on talk pages differs between user groups. Our visualizations demonstrate that low quality edits are primarily performed by IP users, rather than blocked users, and that overall engagement with talk pages has plateaued over the last 10 years across all user groups. Finally, we investigate the feasibility of classifying blocked users using this dataset as an example of future research directions. However, we demonstrate the difficulty of this task and find that additional data or a more advanced model would be needed to classify them, as our approach didn’t provide sufficient information to do this. We anticipate that our visualizations and data extraction process are of interest to the community and will provide researchers with the tools needed to use Wikipedia’s valuable data when resources are limited.
{"title":"Extracting and Visualizing User Engagement on Wikipedia Talk Pages","authors":"Carlin MacKenzie, J. R. Hott","doi":"10.1145/3479986.3479995","DOIUrl":"https://doi.org/10.1145/3479986.3479995","url":null,"abstract":"As Wikipedia has grown in popularity, it is important to investigate its diverse user community and collaborative editorial base. Although all user data, from traffic to user edits, are available for download under a free and open license, it is difficult to work with this data due to its scale. In this paper, we demonstrate how consumer hardware can be used to create a local database of Wikipedia’s full edit history from their public XML data dumps. Using this database, we create and present the first visualizations of how editing on talk pages differs between user groups. Our visualizations demonstrate that low quality edits are primarily performed by IP users, rather than blocked users, and that overall engagement with talk pages has plateaued over the last 10 years across all user groups. Finally, we investigate the feasibility of classifying blocked users using this dataset as an example of future research directions. However, we demonstrate the difficulty of this task and find that additional data or a more advanced model would be needed to classify them, as our approach didn’t provide sufficient information to do this. We anticipate that our visualizations and data extraction process are of interest to the community and will provide researchers with the tools needed to use Wikipedia’s valuable data when resources are limited.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115531907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cross-Classroom Collaborative Project-Based Learning (C3PjBL) requires the formation of project-groups by pairing student-groups across classrooms. Unfortunately, due to the configuration of these groups, the group formation techniques found in the literature are unable to automatically create project-groups for C3PjBL. This paper describes an automatic project-group formation technique for C3PjBL which utilizes clustering to create homogeneous student-groups, based on the students’ perceived technological and higher-order thinking skills (student characteristics). Student-groups, from different classrooms, are then paired using an optimization technique to form project-groups. In our results, we present a comparison of the performance of a random group formation technique and our technique. We observed that automatic group formation using an n-dimensional space of student characteristics and k-means clustering is more effective than random group formation and, the strategy of forming homogeneous student-groups and heterogeneous project-group for C3PjBL creates more compatible group compositions than random grouping.
{"title":"Group Formation in a Cross-Classroom Collaborative Project-Based Learning Environment","authors":"Gail Rolle-Greenidge, P. Walcott","doi":"10.1145/3479986.3479997","DOIUrl":"https://doi.org/10.1145/3479986.3479997","url":null,"abstract":"Cross-Classroom Collaborative Project-Based Learning (C3PjBL) requires the formation of project-groups by pairing student-groups across classrooms. Unfortunately, due to the configuration of these groups, the group formation techniques found in the literature are unable to automatically create project-groups for C3PjBL. This paper describes an automatic project-group formation technique for C3PjBL which utilizes clustering to create homogeneous student-groups, based on the students’ perceived technological and higher-order thinking skills (student characteristics). Student-groups, from different classrooms, are then paired using an optimization technique to form project-groups. In our results, we present a comparison of the performance of a random group formation technique and our technique. We observed that automatic group formation using an n-dimensional space of student characteristics and k-means clustering is more effective than random group formation and, the strategy of forming homogeneous student-groups and heterogeneous project-group for C3PjBL creates more compatible group compositions than random grouping.","PeriodicalId":159312,"journal":{"name":"Proceedings of the 17th International Symposium on Open Collaboration","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129388868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wikipedia is one of the most important sources of encyclopedic knowledge and among the most visited websites on the internet. As a peer-produced knowledge repository, Wikipedia is dependent on its community of contributors. A healthy contributor community and a steady stream of new editors from diverse backgrounds are especially vital for the platform’s future in its endeavour of closing knowledge gaps and combating biases and a lack of diversity that Wikipedia suffers from. Edit-a-thons are social activities aiming to improve content and create new articles on Wikipedia with the purpose of recruitment and onboarding of newcomers. Although edit-a-thons have been facilitated and hosted for many years now, little is known how editors experience such events. In this paper, we study editors experience during a virtual edit-a-thon by applying an ethnomethodological perspective. We use a participatory observation to study incidents of motivation and frustration occurring during the collaborative online writing event. Moreover, we use Hofstede’s 6D Model of National Culture to explore what influence culture has on participants’ actions, expressed feelings and thoughts while interacting with the administrator and with each other. Our findings indicate that the type of motivational factors is very diverse and varies from general motivation to fill in knowledge gaps, in the beginning, to share good resources for citations at later stages of the edit-a-thon. However, participants also experience moments of frustration, especially concerning the usability of the editing interface and when navigating a complex bureaucracy of policies and procedures. Finally, our analysis shows that cultural idiosyncrasies can intensify the frustrating experience of social challenges.
维基百科是百科知识最重要的来源之一，也是互联网上访问量最大的网站之一。作为一个同行生产的知识库，维基百科依赖于它的贡献者社区。一个健康的贡献者社区和来自不同背景的源源不断的新编辑对于这个平台的未来尤其重要，因为它正在努力缩小知识差距，打击偏见和维基百科所遭受的缺乏多样性的问题。编辑马拉松(Edit-a-thons)是一种社会活动，旨在改善维基百科上的内容并创建新文章，目的是招募和加入新成员。尽管编辑马拉松已经被促进和举办了很多年，但很少有人知道编辑们是如何经历这样的活动的。本文从民族方法学的角度研究了虚拟编辑马拉松中的编辑体验。我们使用参与式观察来研究在合作网络写作事件中发生的动机和挫折事件。此外，我们使用Hofstede ' s 6D Model of National Culture来探讨文化对参与者在与管理者互动以及与他人互动时的行为、表达的感受和思想的影响。我们的研究结果表明，激励因素的类型是非常多样化的，从一开始填补知识空白的一般动机，到编辑马拉松后期分享好的引用资源。然而，参与者也会遇到挫折，特别是在编辑界面的可用性以及在复杂的政策和程序中导航时。最后，我们的分析表明，文化特质会加剧对社会挑战的沮丧体验。
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