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      Solidarity and collective issues in remote crowd work: A mixed methods study of the Amazon Mechanical Turk online forum

      1 , 2 , 3
      New Technology, Work and Employment
      Wiley

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          Abstract

          The article deals with collective issues and emerging forms of solidarity in remote crowd work. Using the example of the Amazon Mechanical Turk platform, we analyse communication in one of the oldest online labour forums (OLF) with a mixed‐methods‐approach (web scraping, topic modelling and qualitative content analysis) over a 10‐year period. We identify six broader themes of collective relevance whose importance varies over time. The results indicate that elements of solidarity emerge as crowd workers communicate about deprivations, develop collective orientations, and invoke injustice frames. However, there are no indications of more profound collective activities, which may be due to different types of crowd workers, some of whom have conflicting orientations. We develop assumptions about restrictions of OLF in facilitating collective action and tasks for organisers. The research contributes to a better understanding of mobilisation issues in remote platform labour.

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          Most cited references49

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          The Measurement of Observer Agreement for Categorical Data

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            Finding scientific topics.

            A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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              Structural Topic Models for Open-Ended Survey Responses

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                Author and article information

                Journal
                New Technology, Work and Employment
                New Technol Work Employ
                Wiley
                0268-1072
                1468-005X
                July 2024
                December 08 2023
                July 2024
                : 39
                : 2
                : 281-302
                Affiliations
                [1 ] Institute for Work Science (IAW), Faculty of Social Science, Chair Sociology of the Digital Transformation Ruhr‐University Bochum Bochum Germany
                [2 ] Institute of Sociology, Chair Empirical Social Research Chemnitz University of Technology Chemnitz Germany
                [3 ] Institute of Sociology, Chair Political Sociology Friedrich Schiller University Jena Jena Germany
                Article
                10.1111/ntwe.12285
                513bf3b6-1e13-404a-b50f-b70a1f8e0433
                © 2024

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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